Difference between revisions of "Config:ModelBuilder"

From SUMOwiki
Jump to navigationJump to search
m
m
Line 1: Line 1:
 
== AdaptiveModelBuilder ==
 
== AdaptiveModelBuilder ==
+
 
 
=== rational ===
 
=== rational ===
 
Build rational models
 
Build rational models
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false"><!-- Maximum number of models built before selecting new samples -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <Option key="maximumRunLength" value="30"/><!-- Degeneration of score if a model gets older -->
+
  <!-- Maximum number of models built before selecting new samples -->  
   <Option key="decay" value=".99"/><!-- Size of the best model history -->
+
   <Option key="maximumRunLength" value="30"/>
   <Option key="historySize" value="15"/><!-- One of best, last. When set to best the best `historySize' models are kept,
+
  <!-- Degeneration of score if a model gets older -->
- - when set to last, the last `historySize' models are kept -->
+
   <Option key="decay" value=".99"/>
   <Option key="strategy" value="best"/><!-- <Option key="strategy" value="window"/> -->
+
  <!-- Size of the best model history -->
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalSequentialInterface|RationalSequentialInterface]]"><!-- Bounds for the weights of the rational modeller -->
+
   <Option key="historySize" value="15"/>
       <Option key="weightBounds" value="1,40"/><!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
+
  <!-- One of best, last. When set to best the best `historySize' models are kept,
       <Option key="percentBounds" value="1,100"/><!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
+
    - - when set to last, the last `historySize' models are kept -->
       <Option key="maxDegrees" value="100"/><!-- When randomizing rational flags, what percentage should be set -->
+
   <Option key="strategy" value="best"/>
       <Option key="percentRational" value="70"/><!-- If a variable is named "f" of "frequency" it will be modelled differently, if this is set to auto,
+
 
- - If this field is set to a variable name, that variable will be considered to be the frequency -->
+
  <!-- <Option key="strategy" value="window"/> -->
       <Option key="frequencyVariable" value="auto"/><!-- Base function for interpolation, one of chebyshev, power, legendre -->
+
 
 +
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalSequentialInterface|RationalSequentialInterface]]">
 +
      <!-- Bounds for the weights of the rational modeller -->
 +
       <Option key="weightBounds" value="1,40"/>
 +
      <!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
 +
       <Option key="percentBounds" value="1,100"/>
 +
      <!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
 +
       <Option key="maxDegrees" value="40"/>
 +
      <!-- When randomizing rational flags, what percentage should be set -->
 +
       <Option key="percentRational" value="70"/>
 +
      <!-- If a variable is named "f" of "frequency" it will be modelled differently, if this is set to auto,
 +
      - - If this field is set to a variable name, that variable will be considered to be the frequency -->
 +
       <Option key="frequencyVariable" value="auto"/>
 +
      <!-- Base function for interpolation, one of chebyshev, power, legendre -->
 
       <Option key="basis" value="chebyshev"/>
 
       <Option key="basis" value="chebyshev"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 23: Line 36:
 
=== rationalgenetic ===
 
=== rationalgenetic ===
 
Build rational models using a genetic algorithm
 
Build rational models using a genetic algorithm
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!--See that matlab gads toolbox documentation for more information on the options-->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
 +
  <!--See that matlab gads toolbox documentation for more information on the options-->
 +
  <Option key="restartStrategy" value="continue"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationSize" value="15"/>
 
   <Option key="populationSize" value="15"/>
Line 32: Line 47:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
       
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalGeneticInterface|RationalGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalGeneticInterface|RationalGeneticInterface]]">
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="mutationFcn" value="mutation"/>
 
       <Option key="mutationFcn" value="mutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
       <Option key="creationFcn" value="initial"/><!-- Bounds for the weights of the rational modeller -->
+
       <Option key="creationFcn" value="initial"/>
       <Option key="weightBounds" value="1,40"/><!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
+
          <!-- Bounds for the weights of the rational modeller -->
       <Option key="percentBounds" value="1,100"/><!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
+
       <Option key="weightBounds" value="1,40"/>
       <Option key="maxDegrees" value="100"/><!-- When randomizing rational flags, what percentage should be set -->
+
      <!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
       <Option key="percentRational" value="70"/><!-- If a variable is named "f" of "frequency"  
+
       <Option key="percentBounds" value="1,100"/>
it will be modelled differently, if this is set to auto --><!-- If this field is set to a variable name, that variable will be considered to be the frequency -->
+
      <!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
       <Option key="frequencyVariable" value="auto"/><!-- Base function for interpolation, one of chebyshev, power, legendre -->
+
       <Option key="maxDegrees" value="40"/>
 +
      <!-- When randomizing rational flags, what percentage should be set -->
 +
       <Option key="percentRational" value="70"/>
 +
      <!-- If a variable is named "f" of "frequency"  
 +
        it will be modelled differently, if this is set to auto -->
 +
      <!-- If this field is set to a variable name, that variable will be considered to be the frequency -->
 +
       <Option key="frequencyVariable" value="auto"/>
 +
      <!-- Base function for interpolation, one of chebyshev, power, legendre -->
 
       <Option key="basis" value="chebyshev"/>
 
       <Option key="basis" value="chebyshev"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 49: Line 72:
 
=== RBF ===
 
=== RBF ===
 
Build Radial Basis Function models
 
Build Radial Basis Function models
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false"><!-- Maximum number of models built before selecting new samples -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <Option key="maximumRunLength" value="20"/><!-- Degeneration of score if a model gets older -->
+
  <!-- Maximum number of models built before selecting new samples -->  
   <Option key="decay" value=".9"/><!-- Size of the best model history -->
+
   <Option key="maximumRunLength" value="20"/>
   <Option key="historySize" value="15"/><!-- One of best, last. When set to best the best `historySize' models are kept,
+
  <!-- Degeneration of score if a model gets older -->
- - when set to last, the last `historySize' models are kept -->
+
   <Option key="decay" value=".9"/>
   <Option key="strategy" value="best"/><!-- <Option key="strategy" value="window"/> -->
+
  <!-- Size of the best model history -->
 +
   <Option key="historySize" value="15"/>
 +
  <!-- One of best, last. When set to best the best `historySize' models are kept,
 +
  - - when set to last, the last `historySize' models are kept -->
 +
   <Option key="strategy" value="best"/>
 +
 
 +
  <!-- <Option key="strategy" value="window"/> -->
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFSequentialInterface|BFSequentialInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFSequentialInterface|BFSequentialInterface]]">
 
       <Option key="type" value="RBF"/>
 
       <Option key="type" value="RBF"/>
 +
     
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/><!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
+
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
 +
      <!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 +
     
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="backend" value="AP"/>
 
       <Option key="backend" value="AP"/>
Line 68: Line 101:
 
=== RBFgenetic ===
 
=== RBFgenetic ===
 
Build Radial Basis Function models using a genetic algorithm
 
Build Radial Basis Function models using a genetic algorithm
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!--See that matlab gads toolbox documentation for more information on the options-->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
 +
  <Option key="restartStrategy" value="continue"/>
 +
  <!--See that matlab gads toolbox documentation for more information on the options-->
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationSize" value="15"/>
 
   <Option key="populationSize" value="15"/>
Line 77: Line 112:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
 
       <Option key="type" value="RBF"/>
 
       <Option key="type" value="RBF"/>
 +
     
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="mutationFcn" value="mutation"/>
 
       <Option key="mutationFcn" value="mutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
       <Option key="creationFcn" value="initial"/><!-- Bounds for the shape parameter -->
+
       <Option key="creationFcn" value="initial"/>
 +
     
 +
      <!-- Bounds for the shape parameter -->
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/><!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
+
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
 +
      <!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
       <Option key="regression" value="-1,0,1,2"/><!-- Basisfunction, one of 'multiquadric', 'triharmonic', 'biharmonic' --><!-- Specify which implementation to use, currently, 'Direct', 'AP', 'Greedy' and
+
     
'FastRBF' are supported.
+
       <Option key="regression" value="-1,0,1,2"/>
 
+
      <!-- Basisfunction, one of 'multiquadric', 'triharmonic', 'biharmonic' -->
'Direct' solves the direct problem by inverting the interpolation
+
      <!-- Specify which implementation to use, currently, 'Direct', 'AP', 'Greedy' and
matrix
+
      'FastRBF' are supported.
'AP' uses an alternating projections method when the system gets
+
     
too large. This is *MUCH* slower than 'Direct', and doesn't
+
      'Direct' solves the direct problem by inverting the interpolation
guarantee convergence, use with caution
+
      matrix
'Greedy' uses a one point greedy algorithm for selecting the  
+
      'AP' uses an alternating projections method when the system gets
interpolation centers. Same remark applies as with 'AP'
+
      too large. This is *MUCH* slower than 'Direct', and doesn't
'FastRBF' interfaces the FastRBF library. When using FastRBF,  
+
      guarantee convergence, use with caution
make sure your copy of the software is installed under  
+
      'Greedy' uses a one point greedy algorithm for selecting the  
the src/matlab/contrib directory and that the software  
+
      interpolation centers. Same remark applies as with 'AP'
is licensed properly.
+
      'FastRBF' interfaces the FastRBF library. When using FastRBF,  
The FastRBF matlab toolbox can be found at
+
      make sure your copy of the software is installed under  
http://www.farfieldtechnology.com
+
      the src/matlab/contrib directory and that the software  
-->
+
      is licensed properly.
 +
      The FastRBF matlab toolbox can be found at
 +
      http://www.farfieldtechnology.com
 +
      -->
 
       <Option key="backend" value="AP"/>
 
       <Option key="backend" value="AP"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 109: Line 152:
 
=== DACE ===
 
=== DACE ===
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false"><!-- Maximum number of models built before selecting new samples -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <Option key="maximumRunLength" value="20"/><!-- Degeneration of score if a model gets older -->
+
  <!-- Maximum number of models built before selecting new samples -->  
   <Option key="decay" value=".9"/><!-- Size of the best model history -->
+
   <Option key="maximumRunLength" value="20"/>
   <Option key="historySize" value="15"/><!-- One of best, last. When set to best the best `historySize' models are kept,
+
  <!-- Degeneration of score if a model gets older -->
    - - when set to last, the last `historySize' models are kept -->
+
   <Option key="decay" value=".9"/>
   <Option key="strategy" value="best"/><!-- <Option key="strategy" value="window"/> -->
+
  <!-- Size of the best model history -->
 +
   <Option key="historySize" value="15"/>
 +
  <!-- One of best, last. When set to best the best `historySize' models are kept,
 +
        - - when set to last, the last `historySize' models are kept -->
 +
   <Option key="strategy" value="best"/>
 +
 
 +
  <!-- <Option key="strategy" value="window"/> -->
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFSequentialInterface|BFSequentialInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFSequentialInterface|BFSequentialInterface]]">
 
       <Option key="type" value="DACE"/>
 
       <Option key="type" value="DACE"/>
 +
     
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/><!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
+
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
 +
      <!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 +
     
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="backend" value="AP"/>
 
       <Option key="backend" value="AP"/>
Line 128: Line 181:
 
=== DACEgenetic ===
 
=== DACEgenetic ===
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!--See that matlab gads toolbox documentation for more information on the options-->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
 +
  <!--See that matlab gads toolbox documentation for more information on the options-->
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationSize" value="15"/>
 
   <Option key="populationSize" value="15"/>
Line 137: Line 191:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
 
       <Option key="type" value="DACE"/>
 
       <Option key="type" value="DACE"/>
 +
     
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="mutationFcn" value="mutation"/>
 
       <Option key="mutationFcn" value="mutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
       <Option key="creationFcn" value="initial"/><!-- Bounds for the shape parameter -->
+
       <Option key="creationFcn" value="initial"/>
 +
     
 +
      <!-- Bounds for the shape parameter -->
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/><!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
+
       <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
 +
      <!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic"  min=".1"    max="5"    scale="log"/> -->
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 +
     
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="backend" value="AP"/>
 
       <Option key="backend" value="AP"/>
Line 153: Line 213:
 
=== DACEps ===
 
=== DACEps ===
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="false"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="false"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
Line 162: Line 224:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFOptimizationInterface|BFOptimizationInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFOptimizationInterface|BFOptimizationInterface]]">
       <Option key="type" value="DACE"/><!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
+
       <Option key="type" value="DACE"/>
 +
     
 +
      <!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
 +
     
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 +
     
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="backend" value="AP"/>
 
       <Option key="backend" value="AP"/>
Line 172: Line 239:
 
=== DACEpso ===
 
=== DACEpso ===
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
 
Build DACE models (= functionally equivalent to Kriging, but a custom implementation)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="true"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="true"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 +
 
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
Line 185: Line 255:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFOptimizationInterface|BFOptimizationInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFOptimizationInterface|BFOptimizationInterface]]">
       <Option key="type" value="DACE"/><!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
+
       <Option key="type" value="DACE"/>
 +
     
 +
      <!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
 +
     
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
       <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 +
     
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="regression" value="-1,0,1,2"/>
 
       <Option key="backend" value="AP"/>
 
       <Option key="backend" value="AP"/>
Line 195: Line 270:
 
=== krigingsim ===
 
=== krigingsim ===
 
Build kriging models using Simulated Annealing (requires matlab v7.4)
 
Build kriging models using Simulated Annealing (requires matlab v7.4)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="false"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="false"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
       <Option key="correlationFunction" value="corrgauss"/>
+
       <Option key="correlationFunction" value="corrgauss"/>        
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Line 212: Line 290:
 
=== krigingps ===
 
=== krigingps ===
 
Build kriging models using pattern search
 
Build kriging models using pattern search
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="false"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="false"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
Line 221: Line 301:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
       <Option key="correlationFunction" value="corrgauss"/>
+
       <Option key="correlationFunction" value="corrgauss"/>              
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Line 231: Line 312:
 
=== krigingoptim ===
 
=== krigingoptim ===
 
Build kriging models using the matlab optimization toolbox
 
Build kriging models using the matlab optimization toolbox
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="false"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="false"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
       <Option key="correlationFunction" value="corrgauss"/>
+
       <Option key="correlationFunction" value="corrgauss"/>        
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Line 248: Line 332:
 
=== kriginggenetic ===
 
=== kriginggenetic ===
 
Build kriging models using a genetic algorithm
 
Build kriging models using a genetic algorithm
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="false"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="false"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="populationType" value="doubleVector"/>
 
   <Option key="populationType" value="doubleVector"/>
Line 260: Line 346:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingGeneticInterface|KrigingGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingGeneticInterface|KrigingGeneticInterface]]">
 
       <Option key="creationFcn" value="@gacreationuniform"/>
 
       <Option key="creationFcn" value="@gacreationuniform"/>
 
       <Option key="crossoverFcn" value="@crossoversinglepoint"/>
 
       <Option key="crossoverFcn" value="@crossoversinglepoint"/>
       <Option key="mutationFcn" value="@mutationgaussian"/>
+
       <Option key="mutationFcn" value="@mutationgaussian"/>        
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
 +
     
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
       <Option key="correlationFunction" value="corrgauss"/>
+
       <Option key="correlationFunction" value="corrgauss"/>        
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Line 274: Line 362:
 
=== krigingpso ===
 
=== krigingpso ===
 
Build kriging models using PSO
 
Build kriging models using PSO
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false">
   <Option key="plotOptimSurface" value="false"/><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
   <Option key="plotOptimSurface" value="false"/>
 +
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 +
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
Line 287: Line 378:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
 +
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
       <Option key="correlationFunction" value="corrgauss"/>
+
       <Option key="correlationFunction" value="corrgauss"/>              
  </[[Config:ModelInterface|ModelInterface]]>
+
</[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== krigingrandom ===
 
=== krigingrandom ===
 
Build kriging models randomly
 
Build kriging models randomly
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false"><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 
   <Option key="runSize" value="100"/>
 
   <Option key="runSize" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="lowerThetaBound" value="-5"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="upperThetaBound" value="3"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
 
       <Option key="regressionFunction" value="regpoly0"/>
       <Option key="correlationFunction" value="corrgauss"/>
+
       <Option key="correlationFunction" value="corrgauss"/>              
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== splines ===
 
=== splines ===
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
Build spline models sequentially
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
 
   <Option key="maximumRunLength" value="30"/>
 
   <Option key="maximumRunLength" value="30"/>
Line 316: Line 410:
 
   <Option key="historySize" value="15"/>
 
   <Option key="historySize" value="15"/>
 
   <Option key="strategy" value="best"/>
 
   <Option key="strategy" value="best"/>
 +
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineSequentialInterface|SplineSequentialInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineSequentialInterface|SplineSequentialInterface]]">
       <Option key="smoothingBounds" value="0,1"/>
+
       <Option key="smoothingBounds" value="0,1"/>  
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== splinesgenetic ===
 
=== splinesgenetic ===
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
Build spline models with the genetic modelbuilder
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
Line 334: Line 430:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineGeneticInterface|SplineGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineGeneticInterface|SplineGeneticInterface]]">
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
Line 339: Line 436:
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
       <Option key="smoothingBounds" value="0,1"/>
+
 
 +
       <Option key="smoothingBounds" value="0,1"/>  
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== splinessim ===
 
=== splinessim ===
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
Build spline models using the Simulated Annealing modelbuilder
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
       <Option key="smoothingBounds" value="0,1"/>
+
       <Option key="smoothingBounds" value="0,1"/>    
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== splinesps ===
 
=== splinesps ===
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
Build spline models using the Pattern Search modelbuilder
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
Line 366: Line 467:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
       <Option key="smoothingBounds" value="0,1"/>
+
       <Option key="smoothingBounds" value="0,1"/>          
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Line 373: Line 475:
 
=== splinesoptim ===
 
=== splinesoptim ===
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
       <Option key="smoothingBounds" value="0,1"/>
+
       <Option key="smoothingBounds" value="0,1"/>    
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
Line 386: Line 490:
 
=== annbatch ===
 
=== annbatch ===
 
Maintain a population (batch) of feedforward neural networks and mutation to search the parameter space See the matlab neural network toolbox for more information
 
Maintain a population (batch) of feedforward neural networks and mutation to search the parameter space See the matlab neural network toolbox for more information
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
   <Option key="batchSize" value="10"/><!--One adaptive modeling iteration stops after one of the following two thresholds have been reached-->
+
   <Option key="batchSize" value="10"/>
 +
  <!--One adaptive modeling iteration stops after one of the following two thresholds have been reached-->
 
   <Option key="maxBatches" value="10"/>
 
   <Option key="maxBatches" value="10"/>
 
   <Option key="maxBatchesNoImprovement" value="4"/>
 
   <Option key="maxBatchesNoImprovement" value="4"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNBatchInterface|ANNBatchInterface]]"><!--initial hidden layer dimension-->
+
 
       <Option key="initialSize" value="3,3"/><!--comma separated list of allowed learning rules-->
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNBatchInterface|ANNBatchInterface]]">
       <Option key="allowedLearningRules" value="trainbr"/><!--performance function to use, empty uses training rule default default-->
+
      <!--initial hidden layer dimension-->
       <Option key="performFcn" value=""/><!--how many epochs to train for-->
+
       <Option key="initialSize" value="3,3"/>
       <Option key="epochs" value="300"/><!--max time to train for-->
+
      <!--comma separated list of allowed learning rules-->
       <Option key="trainingTime" value="Inf"/><!--range of initial random weights-->
+
       <Option key="allowedLearningRules" value="trainbr"/>
       <Option key="initWeightRange" value="-0.8,0.8"/><!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
+
      <!--performance function to use, empty uses training rule default default-->
       <Option key="hiddenUnitDelta" value="-2,3"/><!--train until the error reaches this goal-->
+
       <Option key="performFcn" value=""/>
       <Option key="trainingGoal" value="0"/><!--show training progress every x epochs, set to NaN to disable-->
+
      <!--how many epochs to train for-->
       <Option key="trainingProgress" value="NaN"/><!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
+
       <Option key="epochs" value="300"/>
auto: train with early stopping unless regularization is employed
+
      <!--max time to train for-->
Set to any other value for simply training on all the data, doing nothing special -->
+
       <Option key="trainingTime" value="Inf"/>
       <Option key="trainMethod" value="auto"/><!--the training set - validation set - testset ratios-->
+
      <!--range of initial random weights-->
       <Option key="earlyStoppingRatios" value="0.80,0.20,0"/><!-- Transfer function to use for all hidden layers and the output layer
+
       <Option key="initWeightRange" value="-0.8,0.8"/>
So should be a list of max 2 items -->
+
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
 +
       <Option key="hiddenUnitDelta" value="-2,3"/>
 +
      <!--train until the error reaches this goal-->
 +
       <Option key="trainingGoal" value="0"/>
 +
      <!--show training progress every x epochs, set to NaN to disable-->
 +
       <Option key="trainingProgress" value="NaN"/>
 +
      <!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
 +
        auto: train with early stopping unless regularization is employed
 +
        Set to any other value for simply training on all the data, doing nothing special -->
 +
       <Option key="trainMethod" value="auto"/>
 +
      <!--the training set - validation set - testset ratios-->
 +
       <Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
 +
      <!-- Transfer function to use for all hidden layers and the output layer
 +
      So should be a list of max 2 items -->
 
       <Option key="transferFunctionTemplate" value="tansig,purelin"/>
 
       <Option key="transferFunctionTemplate" value="tansig,purelin"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 412: Line 530:
 
=== anngenetic ===
 
=== anngenetic ===
 
Use the matlab gads toolbox to select ANN parameters using a GA
 
Use the matlab gads toolbox to select ANN parameters using a GA
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!--See that matlab gads toolbox documentation for more information on the options-->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="continue"/>
 +
  <!--See that matlab gads toolbox documentation for more information on the options-->
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationSize" value="10"/>
 
   <Option key="populationSize" value="10"/>
Line 423: Line 543:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNGeneticInterface|ANNGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNGeneticInterface|ANNGeneticInterface]]">
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
       <Option key="creationFcn" value="createInitialPopulation"/><!--initial hidden layer dimension-->
+
       <Option key="creationFcn" value="createInitialPopulation"/>
       <Option key="initialSize" value="3,3"/><!--comma separated list of allowed learning rules-->
+
     
       <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/><!--performance function to use, empty uses training rule default default-->
+
      <!--initial hidden layer dimension-->
       <Option key="performFcn" value=""/><!--how many epochs to train for-->
+
       <Option key="initialSize" value="3,3"/>
       <Option key="epochs" value="300"/><!--max time to train for-->
+
      <!--comma separated list of allowed learning rules-->
       <Option key="trainingTime" value="Inf"/><!--range of initial random weights-->
+
       <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
       <Option key="initWeightRange" value="-0.8,0.8"/><!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
+
      <!--performance function to use, empty uses training rule default default-->
       <Option key="hiddenUnitDelta" value="-2,3"/><!--train until the error reaches this goal-->
+
       <Option key="performFcn" value=""/>
       <Option key="trainingGoal" value="0"/><!--show training progress every x epochs, set to NaN to disable-->
+
      <!--how many epochs to train for-->
       <Option key="trainingProgress" value="NaN"/><!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
+
       <Option key="epochs" value="300"/>
auto: train with early stopping unless regularization is employed
+
      <!--max time to train for-->
Set to any other value for simply training on all the data, doing nothing special -->
+
       <Option key="trainingTime" value="Inf"/>
       <Option key="trainMethod" value="auto"/><!--the training set - validation set - testset ratios-->
+
      <!--range of initial random weights-->
       <Option key="earlyStoppingRatios" value="0.80,0.20,0"/><!-- Transfer function to use for all hidden layers and the output layer
+
       <Option key="initWeightRange" value="-0.8,0.8"/>
So should be a list of max 2 items -->
+
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
 +
       <Option key="hiddenUnitDelta" value="-2,3"/>
 +
      <!--train until the error reaches this goal-->
 +
       <Option key="trainingGoal" value="0"/>
 +
      <!--show training progress every x epochs, set to NaN to disable-->
 +
       <Option key="trainingProgress" value="NaN"/>
 +
      <!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
 +
        auto: train with early stopping unless regularization is employed
 +
        Set to any other value for simply training on all the data, doing nothing special -->
 +
       <Option key="trainMethod" value="auto"/>
 +
      <!--the training set - validation set - testset ratios-->
 +
       <Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
 +
      <!-- Transfer function to use for all hidden layers and the output layer
 +
      So should be a list of max 2 items -->
 
       <Option key="transferFunctionTemplate" value="tansig,purelin"/>
 
       <Option key="transferFunctionTemplate" value="tansig,purelin"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 448: Line 582:
 
=== annrandom ===
 
=== annrandom ===
 
Random ANN model builder, usefull as a baseline comparison
 
Random ANN model builder, usefull as a baseline comparison
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false"><!--This many iterations before allowing new samples-->
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 +
  <!--This many iterations before allowing new samples-->
 
   <Option key="runSize" value="10"/>
 
   <Option key="runSize" value="10"/>
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNInterface|ANNInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNInterface|ANNInterface]]">
Line 458: Line 593:
 
=== fanngenetic ===
 
=== fanngenetic ===
 
Use the matlab gads toolbox to select ANN parameters using a GA (based on the FANN library)
 
Use the matlab gads toolbox to select ANN parameters using a GA (based on the FANN library)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!--See that matlab gads toolbox documentation for more information on the options-->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="populationType" value="custom"/>
+
   <Option key="restartStrategy" value="continue"/>
   <Option key="populationSize" value="10"/>
+
  <!--See that matlab gads toolbox documentation for more information on the options-->    
   <Option key="crossoverFraction" value="0.7"/>
+
   <Option key="populationType" value="custom"/>    
   <Option key="maxGenerations" value="10"/>
+
   <Option key="populationSize" value="10"/>    
   <Option key="eliteCount" value="1"/>
+
   <Option key="crossoverFraction" value="0.7"/>    
   <Option key="stallGenLimit" value="4"/>
+
   <Option key="maxGenerations" value="10"/>    
   <Option key="stallTimeLimit" value="Inf"/>
+
   <Option key="eliteCount" value="1"/>    
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#FANNGeneticInterface|FANNGeneticInterface]]">
+
   <Option key="stallGenLimit" value="4"/>    
       <Option key="crossoverFcn" value="simpleCrossover"/>
+
   <Option key="stallTimeLimit" value="Inf"/>    
       <Option key="mutationFcn" value="simpleMutation"/>
+
 
       <Option key="constraintFcn" value="[]"/>
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#FANNGeneticInterface|FANNGeneticInterface]]">    
       <Option key="creationFcn" value="createInitialPopulation"/><!--initial hidden layer dimension-->
+
       <Option key="crossoverFcn" value="simpleCrossover"/>    
       <Option key="initialSize" value="4,4"/><!--how many epochs to train for-->
+
       <Option key="mutationFcn" value="simpleMutation"/>    
       <Option key="epochs" value="1500"/><!--range of initial random weights-->
+
       <Option key="constraintFcn" value="[]"/>    
       <Option key="initWeightRange" value="-0.8,0.8"/><!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
+
       <Option key="creationFcn" value="createInitialPopulation"/>    
       <Option key="hiddenUnitDelta" value="-2,2"/><!--train until the error reaches this goal-->
+
     
       <Option key="trainingGoal" value="0"/>
+
      <!--initial hidden layer dimension-->    
   </[[Config:ModelInterface|ModelInterface]]>
+
       <Option key="initialSize" value="4,4"/>    
 +
      <!--how many epochs to train for-->    
 +
       <Option key="epochs" value="1500"/>    
 +
      <!--range of initial random weights-->    
 +
       <Option key="initWeightRange" value="-0.8,0.8"/>    
 +
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->    
 +
       <Option key="hiddenUnitDelta" value="-2,2"/>    
 +
      <!--train until the error reaches this goal-->    
 +
       <Option key="trainingGoal" value="0"/>    
 +
   </[[Config:ModelInterface|ModelInterface]]>    
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== nanngenetic ===
 
=== nanngenetic ===
 
Use the matlab gads toolbox to select ANN parameters using a GA (based on the NNSYSID library)
 
Use the matlab gads toolbox to select ANN parameters using a GA (based on the NNSYSID library)
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!--See that matlab gads toolbox documentation for more information on the options-->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="populationType" value="custom"/>
+
   <Option key="restartStrategy" value="continue"/>
   <Option key="populationSize" value="10"/>
+
  <!--See that matlab gads toolbox documentation for more information on the options-->    
   <Option key="crossoverFraction" value="0.7"/>
+
   <Option key="populationType" value="custom"/>    
   <Option key="maxGenerations" value="10"/>
+
   <Option key="populationSize" value="10"/>    
   <Option key="eliteCount" value="1"/>
+
   <Option key="crossoverFraction" value="0.7"/>    
   <Option key="stallGenLimit" value="4"/>
+
   <Option key="maxGenerations" value="10"/>    
   <Option key="stallTimeLimit" value="Inf"/>
+
   <Option key="eliteCount" value="1"/>    
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#NANNGeneticInterface|NANNGeneticInterface]]">
+
   <Option key="stallGenLimit" value="4"/>    
       <Option key="crossoverFcn" value="simpleCrossover"/>
+
   <Option key="stallTimeLimit" value="Inf"/>    
       <Option key="mutationFcn" value="simpleMutation"/>
+
 
       <Option key="constraintFcn" value="[]"/>
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#NANNGeneticInterface|NANNGeneticInterface]]">    
       <Option key="creationFcn" value="createInitialPopulation"/><!--initial hidden layer dimension-->
+
       <Option key="crossoverFcn" value="simpleCrossover"/>    
       <Option key="initialSize" value="10"/><!--how many epochs to train for-->
+
       <Option key="mutationFcn" value="simpleMutation"/>    
       <Option key="epochs" value="500"/><!--range of initial random weights-->
+
       <Option key="constraintFcn" value="[]"/>    
       <Option key="initWeightRange" value="-0.8,0.8"/><!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
+
       <Option key="creationFcn" value="createInitialPopulation"/>    
       <Option key="hiddenUnitDelta" value="-2,3"/><!-- pruning techniques used : 0: none, 1: Mag Threshold, 2: Iterative Mag, 3: OBD, 4: OBS -->
+
     
       <Option key="allowedPruneTechniques" value="0,1,2,3,4"/><!-- threshold for magnitude based pruning -->
+
      <!--initial hidden layer dimension-->    
       <Option key="threshold" value="0.2"/><!-- retrain epochs while pruning-->
+
       <Option key="initialSize" value="10"/>    
       <Option key="retrain" value="50"/>
+
      <!--how many epochs to train for-->    
   </[[Config:ModelInterface|ModelInterface]]>
+
       <Option key="epochs" value="500"/>    
 +
      <!--range of initial random weights-->    
 +
       <Option key="initWeightRange" value="-0.8,0.8"/>    
 +
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->    
 +
       <Option key="hiddenUnitDelta" value="-2,3"/>    
 +
      <!-- pruning techniques used : 0: none, 1: Mag Threshold, 2: Iterative Mag, 3: OBD, 4: OBS -->    
 +
       <Option key="allowedPruneTechniques" value="0,1,2,3,4"/>
 +
      <!-- threshold for magnitude based pruning -->    
 +
       <Option key="threshold" value="0.2"/>    
 +
      <!-- retrain epochs while pruning-->    
 +
       <Option key="retrain" value="50"/>    
 +
   </[[Config:ModelInterface|ModelInterface]]>    
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>
 
=== lssvmgenetic ===
 
=== lssvmgenetic ===
 
Use the matlab gads toolbox to select LSSVM parameters using a GA
 
Use the matlab gads toolbox to select LSSVM parameters using a GA
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the matlab gads toolbox documentation for more information on the options--><!--<Option key="populationType" value="doubleVector"/>-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the matlab gads toolbox documentation for more information on the options-->
 +
  <!--<Option key="populationType" value="doubleVector"/>-->
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationSize" value="10"/>
 
   <Option key="populationSize" value="10"/>
Line 524: Line 683:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]"><!--<Option key="creationFcn" value="@gacreationuniform"/>
+
 
<Option key="crossoverFcn" value="@crossoversinglepoint"/>
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
<Option key="mutationFcn" value="@mutationgaussian"/>-->
+
      <!--<Option key="creationFcn" value="@gacreationuniform"/>
 +
      <Option key="crossoverFcn" value="@crossoversinglepoint"/>
 +
      <Option key="mutationFcn" value="@mutationgaussian"/>-->
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
 +
     
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 540: Line 702:
 
=== lssvmps ===
 
=== lssvmps ===
 
Use the matlab gads toolbox to select LSSVM parameters using Pattern Search
 
Use the matlab gads toolbox to select LSSVM parameters using Pattern Search
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See that matlab gads toolbox documentation for more information on the options-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See that matlab gads toolbox documentation for more information on the options-->
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
Line 559: Line 725:
 
=== lssvmoptim ===
 
=== lssvmoptim ===
 
Use the matlab optimization toolbox to select LSSVM parameters
 
Use the matlab optimization toolbox to select LSSVM parameters
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the interface matlab file and the optimization toolbox documentation for more information on the options-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the interface matlab file and the optimization toolbox documentation for more information on the options-->
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
Line 576: Line 746:
 
=== lssvmpso ===
 
=== lssvmpso ===
 
Use the PSO toolbox to select LSSVM parameters using Particle Swarm Optimization
 
Use the PSO toolbox to select LSSVM parameters using Particle Swarm Optimization
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 
   <Option key="plotOptimSurface" value="false"/>
 
   <Option key="plotOptimSurface" value="false"/>
 +
     
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
Line 589: Line 762:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
Line 599: Line 773:
 
=== lssvmsim ===
 
=== lssvmsim ===
 
Use the matlab gads toolbox to select LSSVM parameters using simulated annealing
 
Use the matlab gads toolbox to select LSSVM parameters using simulated annealing
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
Line 616: Line 794:
 
=== lssvmdirect ===
 
=== lssvmdirect ===
 
Use the DIviding RECtangles algorithm to optimize the LS-SVM hyperparameters
 
Use the DIviding RECtangles algorithm to optimize the LS-SVM hyperparameters
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 
   <Option key="plotOptimSurface" value="false"/>
 
   <Option key="plotOptimSurface" value="false"/>
 +
 
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
 
       <Option key="maxits" value="500"/>
 
       <Option key="maxits" value="500"/>
 
       <Option key="maxevals" value="100"/>
 
       <Option key="maxevals" value="100"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
Line 635: Line 817:
 
=== lssvmrandom ===
 
=== lssvmrandom ===
 
Generate random LSSVM models
 
Generate random LSSVM models
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 
   <Option key="runSize" value="100"/>
 
   <Option key="runSize" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
Line 648: Line 831:
 
=== svmgenetic ===
 
=== svmgenetic ===
 
Use the matlab gads toolbox to select SVM parameters using a GA
 
Use the matlab gads toolbox to select SVM parameters using a GA
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationSize" value="10"/>
 
   <Option key="populationSize" value="10"/>
Line 660: Line 846:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]"><!--<Option key="creationFcn" value="@gacreationuniform"/>
+
 
<Option key="crossoverFcn" value="@crossoversinglepoint"/>
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
<Option key="mutationFcn" value="@mutationgaussian"/>-->
+
      <!--<Option key="creationFcn" value="@gacreationuniform"/>
 +
      <Option key="crossoverFcn" value="@crossoversinglepoint"/>
 +
      <Option key="mutationFcn" value="@mutationgaussian"/>-->
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
 +
     
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 680: Line 869:
 
=== svmps ===
 
=== svmps ===
 
Use the matlab gads toolbox to select SVM parameters using Pattern Search
 
Use the matlab gads toolbox to select SVM parameters using Pattern Search
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
Line 703: Line 896:
 
=== svmsim ===
 
=== svmsim ===
 
Use the matlab gads toolbox to select SVM parameters using simulated annealing
 
Use the matlab gads toolbox to select SVM parameters using simulated annealing
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
+
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
Line 724: Line 921:
 
=== svmoptim ===
 
=== svmoptim ===
 
Use the matlab optimization toolbox to select SVM parameters
 
Use the matlab optimization toolbox to select SVM parameters
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/><!--See the interface matlab file and the optimization toolbox documentation for more
+
   <Option key="restartStrategy" value="intelligent"/>
information on the options-->
+
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 +
   <Option key="plotOptimSurface" value="false"/>
 +
  <!--See the interface matlab file and the optimization toolbox documentation for more
 +
      information on the options-->
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
Line 746: Line 947:
 
=== svmpso ===
 
=== svmpso ===
 
Use the PSO toolbox to select SVM parameters using Particle Swarm Optimization
 
Use the PSO toolbox to select SVM parameters using Particle Swarm Optimization
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 
   <Option key="plotOptimSurface" value="false"/>
 
   <Option key="plotOptimSurface" value="false"/>
 +
 
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
Line 759: Line 963:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
Line 773: Line 978:
 
=== svmdirect ===
 
=== svmdirect ===
 
Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters
 
Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/><!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
+
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/>
 +
  <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
 
   <Option key="plotOptimSurface" value="false"/>
 
   <Option key="plotOptimSurface" value="false"/>
 +
 
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
 
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
 
       <Option key="maxits" value="500"/>
 
       <Option key="maxits" value="500"/>
 
       <Option key="maxevals" value="100"/>
 
       <Option key="maxevals" value="100"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
Line 796: Line 1,005:
 
=== svmrandom ===
 
=== svmrandom ===
 
Generate random SVMs
 
Generate random SVMs
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 
   <Option key="runSize" value="100"/>
 
   <Option key="runSize" value="100"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
Line 813: Line 1,023:
 
=== rbfnnbatch ===
 
=== rbfnnbatch ===
 
Batch model builder for Radial Basis Function Neural networks See the matlab neural network toolbox for more information
 
Batch model builder for Radial Basis Function Neural networks See the matlab neural network toolbox for more information
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
 
   <Option key="maxBatches" value="10"/>
 
   <Option key="maxBatches" value="10"/>
 
   <Option key="maxBatchesNoImprovement" value="3"/>
 
   <Option key="maxBatchesNoImprovement" value="3"/>
 
   <Option key="batchSize" value="10"/>
 
   <Option key="batchSize" value="10"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNBatchInterface|RBFNNBatchInterface]]"><!--Error goal when constructing the network-->
+
 
       <Option key="goal" value="0"/><!--Initial value for the spread -->
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNBatchInterface|RBFNNBatchInterface]]">
       <Option key="spread" value="1"/><!--Spread bounds -->
+
      <!--Error goal when constructing the network-->
       <Option key="spreadBounds" value="0.0001,2"/><!--Maximum number of neurons to use per network-->
+
       <Option key="goal" value="0"/>
 +
      <!--Initial value for the spread -->
 +
       <Option key="spread" value="1"/>
 +
      <!--Spread bounds -->
 +
       <Option key="spreadBounds" value="0.0001,2"/>
 +
      <!--Maximum number of neurons to use per network-->
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="trainingProgress" value="Inf"/>
 
       <Option key="trainingProgress" value="Inf"/>
Line 829: Line 1,044:
 
=== rbfnngenetic ===
 
=== rbfnngenetic ===
 
Genetic model builder for Radial Basis Function Neural networks See the matlab neural network toolbox for more information
 
Genetic model builder for Radial Basis Function Neural networks See the matlab neural network toolbox for more information
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
Line 840: Line 1,056:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNGeneticInterface|RBFNNGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNGeneticInterface|RBFNNGeneticInterface]]">
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
 
       <Option key="mutationFcn" value="simpleMutation"/>
       <Option key="constraintFcn" value="[]"/><!--Error goal when constructing the network-->
+
       <Option key="constraintFcn" value="[]"/>
       <Option key="goal" value="0"/><!--Initial value for the spread -->
+
     
       <Option key="spread" value="1"/><!--Spread bounds -->
+
      <!--Error goal when constructing the network-->
       <Option key="spreadBounds" value="0.0001,2"/><!--Maximum number of neurons to use per network-->
+
       <Option key="goal" value="0"/>
 +
      <!--Initial value for the spread -->
 +
       <Option key="spread" value="1"/>
 +
      <!--Spread bounds -->
 +
       <Option key="spreadBounds" value="0.0001,2"/>
 +
      <!--Maximum number of neurons to use per network-->
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="trainingProgress" value="Inf"/>
 
       <Option key="trainingProgress" value="Inf"/>
Line 855: Line 1,077:
 
=== rbfnnoptim ===
 
=== rbfnnoptim ===
 
Build Radial Basis Function Neural networks using the Matlab Optimization Toolbox
 
Build Radial Basis Function Neural networks using the Matlab Optimization Toolbox
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="300"/>
 
   <Option key="maxIterations" value="300"/>
 
   <Option key="maxFunEvals" value="300"/>
 
   <Option key="maxFunEvals" value="300"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
+
 
       <Option key="goal" value="0"/><!--Initial value for the spread -->
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
       <Option key="spread" value="1"/><!--Spread bounds -->
+
      <!--Error goal when constructing the network-->
       <Option key="spreadBounds" value="0.0001,3"/><!--Maximum number of neurons to use per network-->
+
       <Option key="goal" value="0"/>
 +
      <!--Initial value for the spread -->
 +
       <Option key="spread" value="1"/>
 +
      <!--Spread bounds -->
 +
       <Option key="spreadBounds" value="0.0001,3"/>
 +
      <!--Maximum number of neurons to use per network-->
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="trainingProgress" value="Inf"/>
 
       <Option key="trainingProgress" value="Inf"/>
Line 872: Line 1,100:
 
=== rbfnnps ===
 
=== rbfnnps ===
 
Build Radial Basis Function Neural networks using Pattern Search
 
Build Radial Basis Function Neural networks using Pattern Search
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
Line 880: Line 1,109:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
+
 
       <Option key="goal" value="0"/><!--Initial value for the spread -->
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
       <Option key="spread" value="1"/><!--Spread bounds -->
+
      <!--Error goal when constructing the network-->
       <Option key="spreadBounds" value="0.0001,3"/><!--Maximum number of neurons to use per network-->
+
       <Option key="goal" value="0"/>
 +
      <!--Initial value for the spread -->
 +
       <Option key="spread" value="1"/>
 +
      <!--Spread bounds -->
 +
       <Option key="spreadBounds" value="0.0001,3"/>
 +
      <!--Maximum number of neurons to use per network-->
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="trainingProgress" value="Inf"/>
 
       <Option key="trainingProgress" value="Inf"/>
Line 891: Line 1,125:
 
=== rbfnnsim ===
 
=== rbfnnsim ===
 
Build Radial Basis Function Neural networks using Pattern Search
 
Build Radial Basis Function Neural networks using Pattern Search
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
+
  <!-- Re-start strategy for resuming the optimization process between sampling iterations.
 +
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxFunEvals" value="100"/>
 
   <Option key="maxFunEvals" value="100"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
+
 
       <Option key="goal" value="0"/><!--Initial value for the spread -->
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
       <Option key="spread" value="1"/><!--Spread bounds -->
+
      <!--Error goal when constructing the network-->
       <Option key="spreadBounds" value="0.0001,3"/><!--Maximum number of neurons to use per network-->
+
       <Option key="goal" value="0"/>
 +
      <!--Initial value for the spread -->
 +
       <Option key="spread" value="1"/>
 +
      <!--Spread bounds -->
 +
       <Option key="spreadBounds" value="0.0001,3"/>
 +
      <!--Maximum number of neurons to use per network-->
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="trainingProgress" value="Inf"/>
 
       <Option key="trainingProgress" value="Inf"/>
Line 908: Line 1,148:
 
=== rbfnnrandom ===
 
=== rbfnnrandom ===
 
Build random RBF neural networks
 
Build random RBF neural networks
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
 
   <Option key="runSize" value="10"/>
 
   <Option key="runSize" value="10"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
+
 
       <Option key="goal" value="0"/><!--Initial value for the spread -->
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
       <Option key="spread" value="1"/><!--Spread bounds -->
+
      <!--Error goal when constructing the network-->
       <Option key="spreadBounds" value="0.0001,3"/><!--Maximum number of neurons to use per network-->
+
       <Option key="goal" value="0"/>
 +
      <!--Initial value for the spread -->
 +
       <Option key="spread" value="1"/>
 +
      <!--Spread bounds -->
 +
       <Option key="spreadBounds" value="0.0001,3"/>
 +
      <!--Maximum number of neurons to use per network-->
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="maxNeurons" value="100"/>
 
       <Option key="trainingProgress" value="Inf"/>
 
       <Option key="trainingProgress" value="Inf"/>
Line 922: Line 1,167:
 
=== heterogenetic ===
 
=== heterogenetic ===
 
A heterogeneous genetic model builder. Uses a genetic algorithm with speciation (island model) to evolve different model types together. The models types compete against each other until the best model prevails.
 
A heterogeneous genetic model builder. Uses a genetic algorithm with speciation (island model) to evolve different model types together. The models types compete against each other until the best model prevails.
<source lang="xml">
+
<source xmlns:saxon="http://icl.com/saxon" lang="xml">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
 
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="populationType" value="custom"/><!-- the population size must match the number of model interfaces minus 1 -->
+
   <Option key="populationType" value="custom"/>
 +
  <!-- the population size must match the number of model interfaces minus 1 -->
 
   <Option key="populationSize" value="10,10,10"/>
 
   <Option key="populationSize" value="10,10,10"/>
 
   <Option key="maxGenerations" value="10"/>
 
   <Option key="maxGenerations" value="10"/>
   <Option key="crossoverFraction" value="0.7"/>
+
   <Option key="crossoverFraction" value="0.7"/>  
 
   <Option key="eliteCount" value="1"/>
 
   <Option key="eliteCount" value="1"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
Line 933: Line 1,179:
 
   <Option key="migrationDirection" value="forward"/>
 
   <Option key="migrationDirection" value="forward"/>
 
   <Option key="migrationFraction" value="0.1"/>
 
   <Option key="migrationFraction" value="0.1"/>
   <Option key="migrationInterval" value="3"/><!-- Do we want to prevent any model type going completely extinct -->
+
   <Option key="migrationInterval" value="3"/>
   <Option key="extinctionPrevention" value="no"/>
+
  <!-- Do we want to prevent any model type going completely extinct -->
 +
   <Option key="extinctionPrevention" value="no"/>  
 +
 
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#HeterogeneousGeneticInterface|HeterogeneousGeneticInterface]]">
 
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#HeterogeneousGeneticInterface|HeterogeneousGeneticInterface]]">
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
Line 940: Line 1,188:
 
       <Option key="mutationFcn" value="mutate"/>
 
       <Option key="mutationFcn" value="mutate"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
 +
     
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#EnsembleGeneticInterface|EnsembleGeneticInterface]]">
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#EnsembleGeneticInterface|EnsembleGeneticInterface]]">
 
         <Option key="crossoverFcn" value="simpleCrossover"/>
 
         <Option key="crossoverFcn" value="simpleCrossover"/>
         <Option key="mutationFcn" value="simpleMutation"/><!-- the maximum ensemble size -->
+
         <Option key="mutationFcn" value="simpleMutation"/>
         <Option key="maxSize" value="4"/><!-- Ensemble members should differ this much percent -->
+
        <!-- the maximum ensemble size -->
 +
         <Option key="maxSize" value="4"/>
 +
        <!-- Ensemble members should differ this much percent -->
 
         <Option key="equalityThreshold" value="0.05"/>
 
         <Option key="equalityThreshold" value="0.05"/>
 
       </[[Config:ModelInterface|ModelInterface]]>
 
       </[[Config:ModelInterface|ModelInterface]]>
 +
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
 
         <Option key="creationFcn" value="createInitialPopulation"/>
 
         <Option key="creationFcn" value="createInitialPopulation"/>
Line 951: Line 1,203:
 
         <Option key="mutationFcn" value="simpleMutation"/>
 
         <Option key="mutationFcn" value="simpleMutation"/>
 
         <Option key="constraintFcn" value="[]"/>
 
         <Option key="constraintFcn" value="[]"/>
 +
       
 
         <Option key="backend" value="libSVM"/>
 
         <Option key="backend" value="libSVM"/>
 
         <Option key="type" value="epsilon-SVR"/>
 
         <Option key="type" value="epsilon-SVR"/>
Line 960: Line 1,213:
 
         <Option key="stoppingTolerance" value="1e-5"/>
 
         <Option key="stoppingTolerance" value="1e-5"/>
 
       </[[Config:ModelInterface|ModelInterface]]>
 
       </[[Config:ModelInterface|ModelInterface]]>
 +
     
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalGeneticInterface|RationalGeneticInterface]]">
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalGeneticInterface|RationalGeneticInterface]]">
 
         <Option key="crossoverFcn" value="crossover"/>
 
         <Option key="crossoverFcn" value="crossover"/>
Line 971: Line 1,225:
 
         <Option key="basis" value="chebyshev"/>
 
         <Option key="basis" value="chebyshev"/>
 
       </[[Config:ModelInterface|ModelInterface]]>
 
       </[[Config:ModelInterface|ModelInterface]]>
 +
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
 
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
 
         <Option key="type" value="RBF"/>
 
         <Option key="type" value="RBF"/>
 +
 
         <Option key="crossoverFcn" value="crossover"/>
 
         <Option key="crossoverFcn" value="crossover"/>
 
         <Option key="mutationFcn" value="mutation"/>
 
         <Option key="mutationFcn" value="mutation"/>
 
         <Option key="constraintFcn" value="[]"/>
 
         <Option key="constraintFcn" value="[]"/>
 
         <Option key="creationFcn" value="initial"/>
 
         <Option key="creationFcn" value="initial"/>
 +
       
 
         <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
         <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
 
         <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
 
         <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
 
         <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 
         <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
 +
       
 
         <Option key="regression" value="-1,0,1,2"/>
 
         <Option key="regression" value="-1,0,1,2"/>
 
         <Option key="backend" value="Direct"/>
 
         <Option key="backend" value="Direct"/>
       </[[Config:ModelInterface|ModelInterface]]>
+
       </[[Config:ModelInterface|ModelInterface]]>        
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>
 
</source>
 
</source>

Revision as of 13:55, 13 February 2008

AdaptiveModelBuilder

rational

Build rational models

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <!-- Maximum number of models built before selecting new samples -->   
   <Option key="maximumRunLength" value="30"/>
   <!-- Degeneration of score if a model gets older -->
   <Option key="decay" value=".99"/>
   <!-- Size of the best model history -->
   <Option key="historySize" value="15"/>
   <!-- One of best, last. When set to best the best `historySize' models are kept,
    - - when set to last, the last `historySize' models are kept -->
   <Option key="strategy" value="best"/>

   <!-- <Option key="strategy" value="window"/> -->
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalSequentialInterface|RationalSequentialInterface]]">
      <!-- Bounds for the weights of the rational modeller -->
      <Option key="weightBounds" value="1,40"/>
      <!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
      <Option key="percentBounds" value="1,100"/>
      <!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
      <Option key="maxDegrees" value="40"/>
      <!-- When randomizing rational flags, what percentage should be set -->
      <Option key="percentRational" value="70"/>
      <!-- If a variable is named "f" of "frequency" it will be modelled differently, if this is set to auto,
       - - If this field is set to a variable name, that variable will be considered to be the frequency -->
      <Option key="frequencyVariable" value="auto"/>
      <!-- Base function for interpolation, one of chebyshev, power, legendre -->
      <Option key="basis" value="chebyshev"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rationalgenetic

Build rational models using a genetic algorithm

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="restartStrategy" value="continue"/>
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="15"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
         
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalGeneticInterface|RationalGeneticInterface]]">
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="constraintFcn" value="[]"/>
      <Option key="creationFcn" value="initial"/>
          <!-- Bounds for the weights of the rational modeller -->
      <Option key="weightBounds" value="1,40"/>
      <!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
      <Option key="percentBounds" value="1,100"/>
      <!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
      <Option key="maxDegrees" value="40"/>
      <!-- When randomizing rational flags, what percentage should be set -->
      <Option key="percentRational" value="70"/>
      <!-- If a variable is named "f" of "frequency" 
         it will be modelled differently, if this is set to auto -->
      <!-- If this field is set to a variable name, that variable will be considered to be the frequency -->
      <Option key="frequencyVariable" value="auto"/>
      <!-- Base function for interpolation, one of chebyshev, power, legendre -->
      <Option key="basis" value="chebyshev"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

RBF

Build Radial Basis Function models

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <!-- Maximum number of models built before selecting new samples -->   
   <Option key="maximumRunLength" value="20"/>
   <!-- Degeneration of score if a model gets older -->
   <Option key="decay" value=".9"/>
   <!-- Size of the best model history -->
   <Option key="historySize" value="15"/>
   <!-- One of best, last. When set to best the best `historySize' models are kept,
   - - when set to last, the last `historySize' models are kept -->
   <Option key="strategy" value="best"/>
   
   <!-- <Option key="strategy" value="window"/> -->
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFSequentialInterface|BFSequentialInterface]]">
      <Option key="type" value="RBF"/>
      
      <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
      <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
      <!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic"   min=".1"    max="5"    scale="log"/> -->
      <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
      
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

RBFgenetic

Build Radial Basis Function models using a genetic algorithm

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="15"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
      <Option key="type" value="RBF"/>
      
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="constraintFcn" value="[]"/>
      <Option key="creationFcn" value="initial"/>
      
      <!-- Bounds for the shape parameter -->
      <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
      <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
      <!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic"   min=".1"    max="5"    scale="log"/> -->
      <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
      
      <Option key="regression" value="-1,0,1,2"/>
      <!-- Basisfunction, one of 'multiquadric', 'triharmonic', 'biharmonic' -->
      <!-- Specify which implementation to use, currently, 'Direct', 'AP', 'Greedy' and
      'FastRBF' are supported.
      
      'Direct' solves the direct problem by inverting the interpolation
      matrix
      'AP' uses an alternating projections method when the system gets
      too large. This is *MUCH* slower than 'Direct', and doesn't
      guarantee convergence, use with caution
      'Greedy' uses a one point greedy algorithm for selecting the 
      interpolation centers. Same remark applies as with 'AP'
      'FastRBF' interfaces the FastRBF library. When using FastRBF, 
      make sure your copy of the software is installed under 
      the src/matlab/contrib directory and that the software 
      is licensed properly.
      The FastRBF matlab toolbox can be found at
      http://www.farfieldtechnology.com
      -->
      <Option key="backend" value="AP"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

DACE

Build DACE models (= functionally equivalent to Kriging, but a custom implementation)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <!-- Maximum number of models built before selecting new samples -->   
   <Option key="maximumRunLength" value="20"/>
   <!-- Degeneration of score if a model gets older -->
   <Option key="decay" value=".9"/>
   <!-- Size of the best model history -->
   <Option key="historySize" value="15"/>
   <!-- One of best, last. When set to best the best `historySize' models are kept,
        - - when set to last, the last `historySize' models are kept -->
   <Option key="strategy" value="best"/>
   
   <!-- <Option key="strategy" value="window"/> -->
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFSequentialInterface|BFSequentialInterface]]">
      <Option key="type" value="DACE"/>
      
      <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
      <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
      <!--<[[Config:BasisFunction|BasisFunction]] name="biharmonic"   min=".1"    max="5"    scale="log"/> -->
      <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
      
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

DACEgenetic

Build DACE models (= functionally equivalent to Kriging, but a custom implementation)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="15"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
      <Option key="type" value="DACE"/>
      
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="constraintFcn" value="[]"/>
      <Option key="creationFcn" value="initial"/>
      
      <!-- Bounds for the shape parameter -->
      <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
      <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
      <!-- <[[Config:BasisFunction|BasisFunction]] name="biharmonic"   min=".1"    max="5"    scale="log"/> -->
      <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
      
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

DACEps

Build DACE models (= functionally equivalent to Kriging, but a custom implementation)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFOptimizationInterface|BFOptimizationInterface]]">
      <Option key="type" value="DACE"/>
      
      <!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
      
      <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
      
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

DACEpso

Build DACE models (= functionally equivalent to Kriging, but a custom implementation)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="true"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
      <Option key="typePSO" value="0"/>
      <Option key="seedPSO" value="1"/>
      <Option key="popSize" value="10"/>
      <Option key="maxiters" value="10"/>
      <Option key="epochInertia" value="8"/>
      <Option key="gradientTermination" value="8"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFOptimizationInterface|BFOptimizationInterface]]">
      <Option key="type" value="DACE"/>
      
      <!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
      
      <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
      
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingsim

Build kriging models using Simulated Annealing (requires matlab v7.4)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingps

Build kriging models using pattern search

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>               
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingoptim

Build kriging models using the matlab optimization toolbox

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

kriginggenetic

Build kriging models using a genetic algorithm

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="populationType" value="doubleVector"/>
   <Option key="populationSize" value="10"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>

   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingGeneticInterface|KrigingGeneticInterface]]">
      <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoversinglepoint"/>
      <Option key="mutationFcn" value="@mutationgaussian"/>         
      <Option key="constraintFcn" value="[]"/>
      
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingpso

Build kriging models using PSO

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>

   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
      <Option key="typePSO" value="0"/>
      <Option key="seedPSO" value="1"/>
      <Option key="popSize" value="10"/>
      <Option key="maxiters" value="10"/>
      <Option key="epochInertia" value="8"/>
      <Option key="gradientTermination" value="8"/>
   </[[Config:Optimizer|Optimizer]]>

   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>               
</[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingrandom

Build kriging models randomly

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="runSize" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#KrigingInterface|KrigingInterface]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>               
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splines

Build spline models sequentially

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
   <Option key="maximumRunLength" value="30"/>
   <Option key="decay" value=".99"/>
   <Option key="historySize" value="15"/>
   <Option key="strategy" value="best"/>

   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineSequentialInterface|SplineSequentialInterface]]">
      <Option key="smoothingBounds" value="0,1"/>   
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinesgenetic

Build spline models with the genetic modelbuilder

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="10"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineGeneticInterface|SplineGeneticInterface]]">
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="simpleCrossover"/>
      <Option key="mutationFcn" value="simpleMutation"/>
      <Option key="constraintFcn" value="[]"/>

      <Option key="smoothingBounds" value="0,1"/>   
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinessim

Build spline models using the Simulated Annealing modelbuilder

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
      <Option key="smoothingBounds" value="0,1"/>      
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinesps

Build spline models using the Pattern Search modelbuilder

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
      <Option key="smoothingBounds" value="0,1"/>            
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinesoptim

+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SplineInterface|SplineInterface]]">
      <Option key="smoothingBounds" value="0,1"/>      
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

annbatch

Maintain a population (batch) of feedforward neural networks and mutation to search the parameter space See the matlab neural network toolbox for more information

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
   <Option key="batchSize" value="10"/>
   <!--One adaptive modeling iteration stops after one of the following two thresholds have been reached-->
   <Option key="maxBatches" value="10"/>
   <Option key="maxBatchesNoImprovement" value="4"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNBatchInterface|ANNBatchInterface]]">
      <!--initial hidden layer dimension-->
      <Option key="initialSize" value="3,3"/>
      <!--comma separated list of allowed learning rules-->
      <Option key="allowedLearningRules" value="trainbr"/>
      <!--performance function to use, empty uses training rule default default-->
      <Option key="performFcn" value=""/>
      <!--how many epochs to train for-->
      <Option key="epochs" value="300"/>
      <!--max time to train for-->
      <Option key="trainingTime" value="Inf"/>
      <!--range of initial random weights-->
      <Option key="initWeightRange" value="-0.8,0.8"/>
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
      <Option key="hiddenUnitDelta" value="-2,3"/>
      <!--train until the error reaches this goal-->
      <Option key="trainingGoal" value="0"/>
      <!--show training progress every x epochs, set to NaN to disable-->
      <Option key="trainingProgress" value="NaN"/>
      <!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
         auto: train with early stopping unless regularization is employed
         Set to any other value for simply training on all the data, doing nothing special -->
      <Option key="trainMethod" value="auto"/>
      <!--the training set - validation set - testset ratios-->
      <Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
      <!-- Transfer function to use for all hidden layers and the output layer
      So should be a list of max 2 items -->
      <Option key="transferFunctionTemplate" value="tansig,purelin"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

anngenetic

Use the matlab gads toolbox to select ANN parameters using a GA

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="10"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNGeneticInterface|ANNGeneticInterface]]">
      <Option key="crossoverFcn" value="simpleCrossover"/>
      <Option key="mutationFcn" value="simpleMutation"/>
      <Option key="constraintFcn" value="[]"/>
      <Option key="creationFcn" value="createInitialPopulation"/>
      
      <!--initial hidden layer dimension-->
      <Option key="initialSize" value="3,3"/>
      <!--comma separated list of allowed learning rules-->
      <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
      <!--performance function to use, empty uses training rule default default-->
      <Option key="performFcn" value=""/>
      <!--how many epochs to train for-->
      <Option key="epochs" value="300"/>
      <!--max time to train for-->
      <Option key="trainingTime" value="Inf"/>
      <!--range of initial random weights-->
      <Option key="initWeightRange" value="-0.8,0.8"/>
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->
      <Option key="hiddenUnitDelta" value="-2,3"/>
      <!--train until the error reaches this goal-->
      <Option key="trainingGoal" value="0"/>
      <!--show training progress every x epochs, set to NaN to disable-->
      <Option key="trainingProgress" value="NaN"/>
      <!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
         auto: train with early stopping unless regularization is employed
         Set to any other value for simply training on all the data, doing nothing special -->
      <Option key="trainMethod" value="auto"/>
      <!--the training set - validation set - testset ratios-->
      <Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
      <!-- Transfer function to use for all hidden layers and the output layer
      So should be a list of max 2 items -->
      <Option key="transferFunctionTemplate" value="tansig,purelin"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

annrandom

Random ANN model builder, usefull as a baseline comparison

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <!--This many iterations before allowing new samples-->
   <Option key="runSize" value="10"/>
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#ANNInterface|ANNInterface]]">
      <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

fanngenetic

Use the matlab gads toolbox to select ANN parameters using a GA (based on the FANN library)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->     
   <Option key="populationType" value="custom"/>     
   <Option key="populationSize" value="10"/>     
   <Option key="crossoverFraction" value="0.7"/>     
   <Option key="maxGenerations" value="10"/>     
   <Option key="eliteCount" value="1"/>     
   <Option key="stallGenLimit" value="4"/>     
   <Option key="stallTimeLimit" value="Inf"/>     
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#FANNGeneticInterface|FANNGeneticInterface]]">     
      <Option key="crossoverFcn" value="simpleCrossover"/>     
      <Option key="mutationFcn" value="simpleMutation"/>     
      <Option key="constraintFcn" value="[]"/>     
      <Option key="creationFcn" value="createInitialPopulation"/>     
      
      <!--initial hidden layer dimension-->     
      <Option key="initialSize" value="4,4"/>     
      <!--how many epochs to train for-->     
      <Option key="epochs" value="1500"/>     
      <!--range of initial random weights-->     
      <Option key="initWeightRange" value="-0.8,0.8"/>     
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->     
      <Option key="hiddenUnitDelta" value="-2,2"/>     
      <!--train until the error reaches this goal-->     
      <Option key="trainingGoal" value="0"/>     
   </[[Config:ModelInterface|ModelInterface]]>     
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

nanngenetic

Use the matlab gads toolbox to select ANN parameters using a GA (based on the NNSYSID library)

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->     
   <Option key="populationType" value="custom"/>     
   <Option key="populationSize" value="10"/>     
   <Option key="crossoverFraction" value="0.7"/>     
   <Option key="maxGenerations" value="10"/>     
   <Option key="eliteCount" value="1"/>     
   <Option key="stallGenLimit" value="4"/>     
   <Option key="stallTimeLimit" value="Inf"/>     
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#NANNGeneticInterface|NANNGeneticInterface]]">     
      <Option key="crossoverFcn" value="simpleCrossover"/>     
      <Option key="mutationFcn" value="simpleMutation"/>     
      <Option key="constraintFcn" value="[]"/>     
      <Option key="creationFcn" value="createInitialPopulation"/>     
      
      <!--initial hidden layer dimension-->     
      <Option key="initialSize" value="10"/>     
      <!--how many epochs to train for-->     
      <Option key="epochs" value="500"/>     
      <!--range of initial random weights-->     
      <Option key="initWeightRange" value="-0.8,0.8"/>     
      <!--mutation changes x neurons at a time (in a random layer) with x in [lb ub]-->     
      <Option key="hiddenUnitDelta" value="-2,3"/>     
      <!-- pruning techniques used : 0: none, 1: Mag Threshold, 2: Iterative Mag, 3: OBD, 4: OBS -->     
      <Option key="allowedPruneTechniques" value="0,1,2,3,4"/>
      <!-- threshold for magnitude based pruning -->     
      <Option key="threshold" value="0.2"/>     
      <!-- retrain epochs while pruning-->     
      <Option key="retrain" value="50"/>     
   </[[Config:ModelInterface|ModelInterface]]>     
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmgenetic

Use the matlab gads toolbox to select LSSVM parameters using a GA

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the matlab gads toolbox documentation for more information on the options-->
   <!--<Option key="populationType" value="doubleVector"/>-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="10"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
      <!--<Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoversinglepoint"/>
      <Option key="mutationFcn" value="@mutationgaussian"/>-->
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="simpleCrossover"/>
      <Option key="mutationFcn" value="simpleMutation"/>
      <Option key="constraintFcn" value="[]"/>
      
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmps

Use the matlab gads toolbox to select LSSVM parameters using Pattern Search

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmoptim

Use the matlab optimization toolbox to select LSSVM parameters

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the interface matlab file and the optimization toolbox documentation for more information on the options-->
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmpso

Use the PSO toolbox to select LSSVM parameters using Particle Swarm Optimization

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
      
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
      <Option key="typePSO" value="0"/>
      <Option key="seedPSO" value="1"/>
      <Option key="popSize" value="10"/>
      <Option key="maxiters" value="10"/>
      <Option key="epochInertia" value="8"/>
      <Option key="gradientTermination" value="8"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmsim

Use the matlab gads toolbox to select LSSVM parameters using simulated annealing

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmdirect

Use the DIviding RECtangles algorithm to optimize the LS-SVM hyperparameters

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
      <Option key="maxits" value="500"/>
      <Option key="maxevals" value="100"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmrandom

Generate random LSSVM models

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <Option key="runSize" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmgenetic

Use the matlab gads toolbox to select SVM parameters using a GA

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="10"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
      <!--<Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoversinglepoint"/>
      <Option key="mutationFcn" value="@mutationgaussian"/>-->
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="simpleCrossover"/>
      <Option key="mutationFcn" value="simpleMutation"/>
      <Option key="constraintFcn" value="[]"/>
      
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmps

Use the matlab gads toolbox to select SVM parameters using Pattern Search

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmsim

Use the matlab gads toolbox to select SVM parameters using simulated annealing

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmoptim

Use the matlab optimization toolbox to select SVM parameters

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   <!--See the interface matlab file and the optimization toolbox documentation for more
      information on the options-->
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmpso

Use the PSO toolbox to select SVM parameters using Particle Swarm Optimization

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#PSOtOptimizer|PSOtOptimizer]]">
      <Option key="typePSO" value="0"/>
      <Option key="seedPSO" value="1"/>
      <Option key="popSize" value="10"/>
      <Option key="maxiters" value="10"/>
      <Option key="epochInertia" value="8"/>
      <Option key="gradientTermination" value="8"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmdirect

Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
      <Option key="maxits" value="500"/>
      <Option key="maxevals" value="100"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmrandom

Generate random SVMs

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <Option key="runSize" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMInterface|SVMInterface]]">
      <Option key="backend" value="libSVM"/>
      <Option key="type" value="epsilon-SVR"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
      <Option key="nu" value="0.01"/>
      <Option key="epsilon" value="0"/>
      <Option key="stoppingTolerance" value="1e-6"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnbatch

Batch model builder for Radial Basis Function Neural networks See the matlab neural network toolbox for more information

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
   <Option key="maxBatches" value="10"/>
   <Option key="maxBatchesNoImprovement" value="3"/>
   <Option key="batchSize" value="10"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNBatchInterface|RBFNNBatchInterface]]">
      <!--Error goal when constructing the network-->
      <Option key="goal" value="0"/>
      <!--Initial value for the spread -->
      <Option key="spread" value="1"/>
      <!--Spread bounds -->
      <Option key="spreadBounds" value="0.0001,2"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="100"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnngenetic

Genetic model builder for Radial Basis Function Neural networks See the matlab neural network toolbox for more information

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="10"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNGeneticInterface|RBFNNGeneticInterface]]">
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="simpleCrossover"/>
      <Option key="mutationFcn" value="simpleMutation"/>
      <Option key="constraintFcn" value="[]"/>
      
      <!--Error goal when constructing the network-->
      <Option key="goal" value="0"/>
      <!--Initial value for the spread -->
      <Option key="spread" value="1"/>
      <!--Spread bounds -->
      <Option key="spreadBounds" value="0.0001,2"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="100"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnoptim

Build Radial Basis Function Neural networks using the Matlab Optimization Toolbox

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="300"/>
   <Option key="maxFunEvals" value="300"/>

   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
      <!--Error goal when constructing the network-->
      <Option key="goal" value="0"/>
      <!--Initial value for the spread -->
      <Option key="spread" value="1"/>
      <!--Spread bounds -->
      <Option key="spreadBounds" value="0.0001,3"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="100"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnps

Build Radial Basis Function Neural networks using Pattern Search

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
      <!--Error goal when constructing the network-->
      <Option key="goal" value="0"/>
      <!--Initial value for the spread -->
      <Option key="spread" value="1"/>
      <!--Spread bounds -->
      <Option key="spreadBounds" value="0.0001,3"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="100"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnsim

Build Radial Basis Function Neural networks using Pattern Search

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="maxIterations" value="500"/>
   <Option key="maxFunEvals" value="100"/>
   
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
      <!--Error goal when constructing the network-->
      <Option key="goal" value="0"/>
      <!--Initial value for the spread -->
      <Option key="spread" value="1"/>
      <!--Spread bounds -->
      <Option key="spreadBounds" value="0.0001,3"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="100"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnrandom

Build random RBF neural networks

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <Option key="runSize" value="10"/>

   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RBFNNInterface|RBFNNInterface]]">
      <!--Error goal when constructing the network-->
      <Option key="goal" value="0"/>
      <!--Initial value for the spread -->
      <Option key="spread" value="1"/>
      <!--Spread bounds -->
      <Option key="spreadBounds" value="0.0001,3"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="100"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

heterogenetic

A heterogeneous genetic model builder. Uses a genetic algorithm with speciation (island model) to evolve different model types together. The models types compete against each other until the best model prevails.

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="populationType" value="custom"/>
   <!-- the population size must match the number of model interfaces minus 1 -->
   <Option key="populationSize" value="10,10,10"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="crossoverFraction" value="0.7"/> 
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   <Option key="migrationDirection" value="forward"/>
   <Option key="migrationFraction" value="0.1"/>
   <Option key="migrationInterval" value="3"/>
   <!-- Do we want to prevent any model type going completely extinct -->
   <Option key="extinctionPrevention" value="no"/>   

   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#HeterogeneousGeneticInterface|HeterogeneousGeneticInterface]]">
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutate"/>
      <Option key="constraintFcn" value="[]"/>
      
      <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#EnsembleGeneticInterface|EnsembleGeneticInterface]]">
         <Option key="crossoverFcn" value="simpleCrossover"/>
         <Option key="mutationFcn" value="simpleMutation"/>
         <!-- the maximum ensemble size -->
         <Option key="maxSize" value="4"/>
         <!-- Ensemble members should differ this much percent -->
         <Option key="equalityThreshold" value="0.05"/>
      </[[Config:ModelInterface|ModelInterface]]>

      <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#SVMGeneticInterface|SVMGeneticInterface]]">
         <Option key="creationFcn" value="createInitialPopulation"/>
         <Option key="crossoverFcn" value="simpleCrossover"/>
         <Option key="mutationFcn" value="simpleMutation"/>
         <Option key="constraintFcn" value="[]"/>
         
         <Option key="backend" value="libSVM"/>
         <Option key="type" value="epsilon-SVR"/>
         <Option key="kernel" value="rbf"/>
         <Option key="kernelParamBounds" value="-4,4"/>
         <Option key="regParamBounds" value="-5,5"/>
         <Option key="nu" value="0.01"/>
         <Option key="epsilon" value="0"/>
         <Option key="stoppingTolerance" value="1e-5"/>
      </[[Config:ModelInterface|ModelInterface]]>
      
      <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#RationalGeneticInterface|RationalGeneticInterface]]">
         <Option key="crossoverFcn" value="crossover"/>
         <Option key="mutationFcn" value="mutation"/>
         <Option key="constraintFcn" value="[]"/>
         <Option key="creationFcn" value="initial"/>
         <Option key="weightBounds" value="1,40"/>
         <Option key="percentBounds" value="1,100"/>
         <Option key="percentRational" value="70"/>
         <Option key="frequencyVariable" value="off"/>
         <Option key="basis" value="chebyshev"/>
      </[[Config:ModelInterface|ModelInterface]]>

      <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface#BFGeneticInterface|BFGeneticInterface]]">
         <Option key="type" value="RBF"/>

         <Option key="crossoverFcn" value="crossover"/>
         <Option key="mutationFcn" value="mutation"/>
         <Option key="constraintFcn" value="[]"/>
         <Option key="creationFcn" value="initial"/>
         
         <[[Config:BasisFunction|BasisFunction]] name="gaussian" min=".1" max="5" scale="log"/>
         <[[Config:BasisFunction|BasisFunction]] name="multiquadric" min=".1" max="5" scale="log"/>
         <[[Config:BasisFunction|BasisFunction]] name="exponential" min=".1,.5" max="5,2" scale="log,lin"/>
         
         <Option key="regression" value="-1,0,1,2"/>
         <Option key="backend" value="Direct"/>
      </[[Config:ModelInterface|ModelInterface]]>         
   </[[Config:ModelInterface|ModelInterface]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>