Difference between revisions of "Config:ModelBuilder"

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Build rational models
 
Build rational models
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rational" type="SequentialModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false"><!-- Maximum number of models built before selecting new samples -->
   <Option key="maximumRunLength" value="30"/>
+
   <Option key="maximumRunLength" value="30"/><!-- Degeneration of score if a model gets older -->
   <Option key="decay" value=".99"/>
+
   <Option key="decay" value=".99"/><!-- Size of the best model history -->
   <Option key="historySize" value="15"/>
+
   <Option key="historySize" value="15"/><!-- One of best, last. When set to best the best `historySize' models are kept,
   <Option key="strategy" value="best"/>
+
- - when set to last, the last `historySize' models are kept -->
   <[[Config:ModelInterface|ModelInterface]] type="RationalSequentialInterface">
+
   <Option key="strategy" value="best"/><!-- <Option key="strategy" value="window"/> -->
       <Option key="weightBounds" value="1,40"/>
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:RationalSequentialInterface|RationalSequentialInterface]]"><!-- Bounds for the weights of the rational modeller -->
       <Option key="percentBounds" value="1,100"/>
+
       <Option key="weightBounds" value="1,40"/><!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
       <Option key="maxDegrees" value="100"/>
+
       <Option key="percentBounds" value="1,100"/><!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
       <Option key="percentRational" value="70"/>
+
       <Option key="maxDegrees" value="100"/><!-- When randomizing rational flags, what percentage should be set -->
       <Option key="frequencyVariable" value="auto"/>
+
       <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 22: Line 24:
 
Build rational models using a genetic algorithm
 
Build rational models using a genetic algorithm
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rationalgenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[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 30: Line 32:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] type="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"/>
+
       <Option key="creationFcn" value="initial"/><!-- Bounds for the weights of the rational modeller -->
       <Option key="weightBounds" value="1,40"/>
+
       <Option key="weightBounds" value="1,40"/><!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
       <Option key="percentBounds" value="1,100"/>
+
       <Option key="percentBounds" value="1,100"/><!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
       <Option key="maxDegrees" value="100"/>
+
       <Option key="maxDegrees" value="100"/><!-- When randomizing rational flags, what percentage should be set -->
       <Option key="percentRational" value="70"/>
+
       <Option key="percentRational" value="70"/><!-- If a variable is named "f" of "frequency"
       <Option key="frequencyVariable" value="auto"/>
+
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 47: Line 50:
 
Build Radial Basis Function models
 
Build Radial Basis Function models
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="RBF" type="SequentialModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false"><!-- Maximum number of models built before selecting new samples -->
   <Option key="maximumRunLength" value="20"/>
+
   <Option key="maximumRunLength" value="20"/><!-- Degeneration of score if a model gets older -->
   <Option key="decay" value=".9"/>
+
   <Option key="decay" value=".9"/><!-- Size of the best model history -->
   <Option key="historySize" value="15"/>
+
   <Option key="historySize" value="15"/><!-- One of best, last. When set to best the best `historySize' models are kept,
   <Option key="strategy" value="best"/>
+
- - when set to last, the last `historySize' models are kept -->
   <[[Config:ModelInterface|ModelInterface]] type="BFSequentialInterface">
+
   <Option key="strategy" value="best"/><!-- <Option key="strategy" value="window"/> -->
 +
   <[[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="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"/>
Line 65: Line 69:
 
Build Radial Basis Function models using a genetic algorithm
 
Build Radial Basis Function models using a genetic algorithm
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="RBFgenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[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 73: Line 77:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] type="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"/><!-- 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="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"/><!-- 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"/>
 
       <Option key="backend" value="AP"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 90: Line 110:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="DACE" type="SequentialModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false"><!-- Maximum number of models built before selecting new samples -->
   <Option key="maximumRunLength" value="20"/>
+
   <Option key="maximumRunLength" value="20"/><!-- Degeneration of score if a model gets older -->
   <Option key="decay" value=".9"/>
+
   <Option key="decay" value=".9"/><!-- Size of the best model history -->
   <Option key="historySize" value="15"/>
+
   <Option key="historySize" value="15"/><!-- One of best, last. When set to best the best `historySize' models are kept,
   <Option key="strategy" value="best"/>
+
    - - when set to last, the last `historySize' models are kept -->
   <[[Config:ModelInterface|ModelInterface]] type="BFSequentialInterface">
+
   <Option key="strategy" value="best"/><!-- <Option key="strategy" value="window"/> -->
 +
   <[[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="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"/>
Line 108: Line 129:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="DACEgenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[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 116: Line 137:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] type="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"/>
+
       <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="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"/>
Line 133: Line 154:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="DACEps" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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 140: Line 162:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   <[[Config:ModelInterface|ModelInterface]] id="dace" type="BFOptimizationInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:BFOptimizationInterface|BFOptimizationInterface]]">
       <Option key="type" value="DACE"/>
+
       <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"/>
Line 151: Line 173:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="DACEpso" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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]] id="pso" type="PSOtOptimizer">
+
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer:PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
 
       <Option key="seedPSO" value="1"/>
 
       <Option key="seedPSO" value="1"/>
Line 162: Line 185:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
   <[[Config:ModelInterface|ModelInterface]] id="dace" type="BFOptimizationInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:BFOptimizationInterface|BFOptimizationInterface]]">
       <Option key="type" value="DACE"/>
+
       <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"/>
Line 173: Line 196:
 
Build kriging models using Simulated Annealing (requires matlab v7.4)
 
Build kriging models using Simulated Annealing (requires matlab v7.4)
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="krigingsim" type="SimAnnealingModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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]] id="kriging" type="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"/>
Line 189: Line 213:
 
Build kriging models using pattern search
 
Build kriging models using pattern search
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="krigingps" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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 196: Line 221:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   <[[Config:ModelInterface|ModelInterface]] id="kriging" type="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"/>
Line 207: Line 232:
 
Build kriging models using the matlab optimization toolbox
 
Build kriging models using the matlab optimization toolbox
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="krigingoptim" type="OptimToolboxModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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]] id="kriging" type="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"/>
Line 223: Line 249:
 
Build kriging models using a genetic algorithm
 
Build kriging models using a genetic algorithm
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="kriginggenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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 233: Line 260:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="kriging" type="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"/>
Line 248: Line 275:
 
Build kriging models using PSO
 
Build kriging models using PSO
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="krigingpso" type="PSOModelBuilder" combineOutputs="false">
+
<[[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"/>
+
   <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]] id="pso" type="PSOtOptimizer">
+
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer:PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
 
       <Option key="seedPSO" value="1"/>
 
       <Option key="seedPSO" value="1"/>
Line 259: Line 287:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
   <[[Config:ModelInterface|ModelInterface]] id="kriging" type="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"/>
Line 270: Line 298:
 
Build kriging models randomly
 
Build kriging models randomly
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="krigingrandom" type="RandomModelBuilder" combineOutputs="false">
+
<[[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]] id="kriging" type="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"/>
Line 283: Line 311:
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="splines" type="SequentialModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="false">
 
   <Option key="maximumRunLength" value="30"/>
 
   <Option key="maximumRunLength" value="30"/>
 
   <Option key="decay" value=".99"/>
 
   <Option key="decay" value=".99"/>
 
   <Option key="historySize" value="15"/>
 
   <Option key="historySize" value="15"/>
 
   <Option key="strategy" value="best"/>
 
   <Option key="strategy" value="best"/>
   <[[Config:ModelInterface|ModelInterface]] type="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]]>
Line 296: Line 324:
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="splinesgenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[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="restartStrategy" value="intelligent"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
Line 305: Line 334:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="spline" type="SplineGeneticInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SplineGeneticInterface|SplineGeneticInterface]]">
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
Line 317: Line 346:
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="splinessim" type="SimAnnealingModelBuilder" combineOutputs="false">
+
<[[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="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]] id="spline" type="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]]>
Line 329: Line 359:
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="splinesps" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[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="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
Line 335: Line 366:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   <[[Config:ModelInterface|ModelInterface]] id="spline" type="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]]>
Line 343: Line 374:
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="splinesoptim" type="OptimToolboxModelBuilder" combineOutputs="false">
+
<[[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="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]] id="spline" type="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]]>
Line 355: Line 387:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="annbatch" type="BatchModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:BatchModelBuilder|BatchModelBuilder]]" combineOutputs="false">
   <Option key="batchSize" value="10"/>
+
   <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]] id="ann" type="ANNBatchInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:ANNBatchInterface|ANNBatchInterface]]"><!--initial hidden layer dimension-->
       <Option key="initialSize" value="3,3"/>
+
       <Option key="initialSize" value="3,3"/><!--comma separated list of allowed learning rules-->
       <Option key="allowedLearningRules" value="trainbr"/>
+
       <Option key="allowedLearningRules" value="trainbr"/><!--performance function to use, empty uses training rule default default-->
       <Option key="performFcn" value=""/>
+
       <Option key="performFcn" value=""/><!--how many epochs to train for-->
       <Option key="epochs" value="300"/>
+
       <Option key="epochs" value="300"/><!--max time to train for-->
       <Option key="trainingTime" value="Inf"/>
+
       <Option key="trainingTime" value="Inf"/><!--range of initial random weights-->
       <Option key="initWeightRange" value="-0.8,0.8"/>
+
       <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"/>
+
       <Option key="hiddenUnitDelta" value="-2,3"/><!--train until the error reaches this goal-->
       <Option key="trainingGoal" value="0"/>
+
       <Option key="trainingGoal" value="0"/><!--show training progress every x epochs, set to NaN to disable-->
       <Option key="trainingProgress" value="NaN"/>
+
       <Option key="trainingProgress" value="NaN"/><!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
       <Option key="trainMethod" value="auto"/>
+
auto: train with early stopping unless regularization is employed
       <Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
+
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 378: Line 413:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="anngenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/><!--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 387: Line 423:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="ann" type="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"/>
+
       <Option key="creationFcn" value="createInitialPopulation"/><!--initial hidden layer dimension-->
       <Option key="initialSize" value="3,3"/>
+
       <Option key="initialSize" value="3,3"/><!--comma separated list of allowed learning rules-->
       <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
+
       <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/><!--performance function to use, empty uses training rule default default-->
       <Option key="performFcn" value=""/>
+
       <Option key="performFcn" value=""/><!--how many epochs to train for-->
       <Option key="epochs" value="300"/>
+
       <Option key="epochs" value="300"/><!--max time to train for-->
       <Option key="trainingTime" value="Inf"/>
+
       <Option key="trainingTime" value="Inf"/><!--range of initial random weights-->
       <Option key="initWeightRange" value="-0.8,0.8"/>
+
       <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"/>
+
       <Option key="hiddenUnitDelta" value="-2,3"/><!--train until the error reaches this goal-->
       <Option key="trainingGoal" value="0"/>
+
       <Option key="trainingGoal" value="0"/><!--show training progress every x epochs, set to NaN to disable-->
       <Option key="trainingProgress" value="NaN"/>
+
       <Option key="trainingProgress" value="NaN"/><!--How to train the network, one of 'auto', 'earlyStopping', 'crossvalidation'
       <Option key="trainMethod" value="auto"/>
+
auto: train with early stopping unless regularization is employed
       <Option key="earlyStoppingRatios" value="0.80,0.20,0"/>
+
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 410: Line 449:
 
Random ANN model builder, usefull as a baseline comparison
 
Random ANN model builder, usefull as a baseline comparison
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="annrandom" type="RandomModelBuilder" combineOutputs="false">
+
<[[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]] id="ann" type="ANNInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:ANNInterface|ANNInterface]]">
 
       <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
 
       <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 420: Line 459:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="fanngenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/><!--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 429: Line 469:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="fann" type="FANNGeneticInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:FANNGeneticInterface|FANNGeneticInterface]]">
 
       <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"/>
+
       <Option key="creationFcn" value="createInitialPopulation"/><!--initial hidden layer dimension-->
       <Option key="initialSize" value="4,4"/>
+
       <Option key="initialSize" value="4,4"/><!--how many epochs to train for-->
       <Option key="epochs" value="1500"/>
+
       <Option key="epochs" value="1500"/><!--range of initial random weights-->
       <Option key="initWeightRange" value="-0.8,0.8"/>
+
       <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"/>
+
       <Option key="hiddenUnitDelta" value="-2,2"/><!--train until the error reaches this goal-->
 
       <Option key="trainingGoal" value="0"/>
 
       <Option key="trainingGoal" value="0"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 445: Line 485:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="nanngenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
 +
   <Option key="restartStrategy" value="intelligent"/><!--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 454: Line 495:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="nann" type="NANNGeneticInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:NANNGeneticInterface|NANNGeneticInterface]]">
 
       <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"/>
+
       <Option key="creationFcn" value="createInitialPopulation"/><!--initial hidden layer dimension-->
       <Option key="initialSize" value="10"/>
+
       <Option key="initialSize" value="10"/><!--how many epochs to train for-->
       <Option key="epochs" value="500"/>
+
       <Option key="epochs" value="500"/><!--range of initial random weights-->
       <Option key="initWeightRange" value="-0.8,0.8"/>
+
       <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"/>
+
       <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"/>
+
       <Option key="allowedPruneTechniques" value="0,1,2,3,4"/><!-- threshold for magnitude based pruning -->
       <Option key="threshold" value="0.2"/>
+
       <Option key="threshold" value="0.2"/><!-- retrain epochs while pruning-->
 
       <Option key="retrain" value="50"/>
 
       <Option key="retrain" value="50"/>
 
   </[[Config:ModelInterface|ModelInterface]]>
 
   </[[Config:ModelInterface|ModelInterface]]>
Line 472: Line 513:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmgenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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 482: Line 524:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="LSSVM" type="SVMGeneticInterface">
+
   <[[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="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
Line 497: Line 541:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmps" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 515: Line 560:
 
Use the matlab optimization toolbox to select LSSVM parameters
 
Use the matlab optimization toolbox to select LSSVM parameters
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmoptim" type="OptimToolboxModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 531: Line 577:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmpso" type="PSOModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    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]] id="pso" type="PSOtOptimizer">
+
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer:PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
 
       <Option key="seedPSO" value="1"/>
 
       <Option key="seedPSO" value="1"/>
Line 542: Line 589:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
   <[[Config:ModelInterface|ModelInterface]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 553: Line 600:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmsim" type="SimAnnealingModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 569: Line 617:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmdirect" type="OptimizerModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    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]] id="opt" type="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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 587: Line 636:
 
Generate random LSSVM models
 
Generate random LSSVM models
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="lssvmrandom" type="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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="backend" value="lssvm"/>
 
       <Option key="kernel" value="rbf"/>
 
       <Option key="kernel" value="rbf"/>
Line 600: Line 649:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmgenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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 610: Line 660:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="SVM" type="SVMGeneticInterface">
+
   <[[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="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
 
       <Option key="crossoverFcn" value="simpleCrossover"/>
Line 629: Line 681:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmps" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:PatternSearchModelBuilder|PatternSearchModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 651: Line 704:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmsim" type="SimAnnealingModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:SimAnnealingModelBuilder|SimAnnealingModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 671: Line 725:
 
Use the matlab optimization toolbox to select SVM parameters
 
Use the matlab optimization toolbox to select SVM parameters
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmoptim" type="OptimToolboxModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:OptimToolboxModelBuilder|OptimToolboxModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="plotOptimSurface" value="false"/>
+
   <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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 691: Line 747:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmpso" type="PSOModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:PSOModelBuilder|PSOModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    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]] id="pso" type="PSOtOptimizer">
+
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer:PSOtOptimizer|PSOtOptimizer]]">
 
       <Option key="typePSO" value="0"/>
 
       <Option key="typePSO" value="0"/>
 
       <Option key="seedPSO" value="1"/>
 
       <Option key="seedPSO" value="1"/>
Line 702: Line 759:
 
       <Option key="gradientTermination" value="8"/>
 
       <Option key="gradientTermination" value="8"/>
 
   </[[Config:Optimizer|Optimizer]]>
 
   </[[Config:Optimizer|Optimizer]]>
   <[[Config:ModelInterface|ModelInterface]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 717: Line 774:
 
Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters
 
Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmdirect" type="OptimizerModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:OptimizerModelBuilder|OptimizerModelBuilder]]" combineOutputs="false"><!-- Re-start strategy for resuming the optimization process between sampling iterations.
   <Option key="restartStrategy" value="intelligent"/>
+
    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]] id="opt" type="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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 739: Line 797:
 
Generate random SVMs
 
Generate random SVMs
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="svmrandom" type="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]] id="SVM" type="SVMInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMInterface|SVMInterface]]">
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="backend" value="libSVM"/>
 
       <Option key="type" value="epsilon-SVR"/>
 
       <Option key="type" value="epsilon-SVR"/>
Line 756: Line 814:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rbfnnbatch" type="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]] id="rbfnn" type="RBFNNBatchInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:RBFNNBatchInterface|RBFNNBatchInterface]]"><!--Error goal when constructing the network-->
       <Option key="goal" value="0"/>
+
       <Option key="goal" value="0"/><!--Initial value for the spread -->
       <Option key="spread" value="1"/>
+
       <Option key="spread" value="1"/><!--Spread bounds -->
       <Option key="spreadBounds" value="0.0001,2"/>
+
       <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 772: Line 830:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rbfnngenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[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="restartStrategy" value="intelligent"/>
 
   <Option key="populationType" value="custom"/>
 
   <Option key="populationType" value="custom"/>
Line 781: Line 840:
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallGenLimit" value="4"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
 
   <Option key="stallTimeLimit" value="Inf"/>
   <[[Config:ModelInterface|ModelInterface]] id="rbfnn" type="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="[]"/>
+
       <Option key="constraintFcn" value="[]"/><!--Error goal when constructing the network-->
       <Option key="goal" value="0"/>
+
       <Option key="goal" value="0"/><!--Initial value for the spread -->
       <Option key="spread" value="1"/>
+
       <Option key="spread" value="1"/><!--Spread bounds -->
       <Option key="spreadBounds" value="0.0001,2"/>
+
       <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 797: Line 856:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rbfnnoptim" type="OptimToolboxModelBuilder" combineOutputs="false">
+
<[[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="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]] id="rbfnn" type="RBFNNInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
       <Option key="goal" value="0"/>
+
       <Option key="goal" value="0"/><!--Initial value for the spread -->
       <Option key="spread" value="1"/>
+
       <Option key="spread" value="1"/><!--Spread bounds -->
       <Option key="spreadBounds" value="0.0001,3"/>
+
       <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 813: Line 873:
 
Build Radial Basis Function Neural networks using Pattern Search
 
Build Radial Basis Function Neural networks using Pattern Search
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rbfnnps" type="PatternSearchModelBuilder" combineOutputs="false">
+
<[[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="restartStrategy" value="intelligent"/>
 
   <Option key="maxIterations" value="500"/>
 
   <Option key="maxIterations" value="500"/>
Line 819: Line 880:
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="searchMethod" value="GPSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
 
   <Option key="pollMethod" value="MADSPositiveBasis2N"/>
   <[[Config:ModelInterface|ModelInterface]] id="rbfnn" type="RBFNNInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
       <Option key="goal" value="0"/>
+
       <Option key="goal" value="0"/><!--Initial value for the spread -->
       <Option key="spread" value="1"/>
+
       <Option key="spread" value="1"/><!--Spread bounds -->
       <Option key="spreadBounds" value="0.0001,3"/>
+
       <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 831: Line 892:
 
Build Radial Basis Function Neural networks using Pattern Search
 
Build Radial Basis Function Neural networks using Pattern Search
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rbfnnsim" type="SimAnnealingModelBuilder" combineOutputs="false">
+
<[[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="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]] id="rbfnn" type="RBFNNInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
       <Option key="goal" value="0"/>
+
       <Option key="goal" value="0"/><!--Initial value for the spread -->
       <Option key="spread" value="1"/>
+
       <Option key="spread" value="1"/><!--Spread bounds -->
       <Option key="spreadBounds" value="0.0001,3"/>
+
       <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 847: Line 909:
 
Build random RBF neural networks
 
Build random RBF neural networks
 
<source lang="xml">
 
<source lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="rbfnnrandom" type="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]] id="rbfnn" type="RBFNNInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:RBFNNInterface|RBFNNInterface]]"><!--Error goal when constructing the network-->
       <Option key="goal" value="0"/>
+
       <Option key="goal" value="0"/><!--Initial value for the spread -->
       <Option key="spread" value="1"/>
+
       <Option key="spread" value="1"/><!--Spread bounds -->
       <Option key="spreadBounds" value="0.0001,3"/>
+
       <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 861: Line 923:
 
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 lang="xml">
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] id="heterogenetic" type="GeneticModelBuilder" combineOutputs="false">
+
<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder:GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="populationType" value="custom"/>
+
   <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"/>
Line 871: Line 933:
 
   <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"/>
+
   <Option key="migrationInterval" value="3"/><!-- Do we want to prevent any model type going completely extinct -->
 
   <Option key="extinctionPrevention" value="no"/>
 
   <Option key="extinctionPrevention" value="no"/>
   <[[Config:ModelInterface|ModelInterface]] id="hetero" type="HeterogeneousGeneticInterface">
+
   <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:HeterogeneousGeneticInterface|HeterogeneousGeneticInterface]]">
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="creationFcn" value="createInitialPopulation"/>
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="crossoverFcn" value="crossover"/>
 
       <Option key="mutationFcn" value="mutate"/>
 
       <Option key="mutationFcn" value="mutate"/>
 
       <Option key="constraintFcn" value="[]"/>
 
       <Option key="constraintFcn" value="[]"/>
       <[[Config:ModelInterface|ModelInterface]] id="Ensemble" type="EnsembleGeneticInterface">
+
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:EnsembleGeneticInterface|EnsembleGeneticInterface]]">
 
         <Option key="crossoverFcn" value="simpleCrossover"/>
 
         <Option key="crossoverFcn" value="simpleCrossover"/>
         <Option key="mutationFcn" value="simpleMutation"/>
+
         <Option key="mutationFcn" value="simpleMutation"/><!-- the maximum ensemble size -->
         <Option key="maxSize" value="4"/>
+
         <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]] id="SVM" type="SVMGeneticInterface">
+
       <[[Config:ModelInterface|ModelInterface]] type="[[ModelInterface:SVMGeneticInterface|SVMGeneticInterface]]">
 
         <Option key="creationFcn" value="createInitialPopulation"/>
 
         <Option key="creationFcn" value="createInitialPopulation"/>
 
         <Option key="crossoverFcn" value="simpleCrossover"/>
 
         <Option key="crossoverFcn" value="simpleCrossover"/>
Line 898: Line 960:
 
         <Option key="stoppingTolerance" value="1e-5"/>
 
         <Option key="stoppingTolerance" value="1e-5"/>
 
       </[[Config:ModelInterface|ModelInterface]]>
 
       </[[Config:ModelInterface|ModelInterface]]>
       <[[Config:ModelInterface|ModelInterface]] type="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"/>
Line 909: Line 971:
 
         <Option key="basis" value="chebyshev"/>
 
         <Option key="basis" value="chebyshev"/>
 
       </[[Config:ModelInterface|ModelInterface]]>
 
       </[[Config:ModelInterface|ModelInterface]]>
       <[[Config:ModelInterface|ModelInterface]] type="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"/>

Revision as of 10:16, 8 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="100"/><!-- 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="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="100"/><!-- 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"><!--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

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

<[[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

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

<[[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

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

<[[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

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

<[[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="intelligent"/><!--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="intelligent"/><!--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="intelligent"/><!--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]]>