Config:ModelBuilder

Generated for SUMO toolbox version 6.1. We are well aware that documentation is not always complete and possibly even out of date in some cases. We try to document everything as best we can but much is limited by available time and manpower. We are are a university research group after all. The most up to date documentation can always be found (if not here) in the default.xml configuration file and, of course, in the source files. If something is unclear please dont hesitate to ask.

AdaptiveModelBuilder

rational

Build rational models using a custom stochastic hillclimber to select the model parameters

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#SequentialModelBuilder|SequentialModelBuilder]]" combineOutputs="true">
   <!-- 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:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
      <!-- 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="80"/>
      <!-- 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="power"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rationalgenetic

Build rational models using a genetic algorithm

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="restartStrategy" value="continue"/>
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="30"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="20"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="5"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="creationFcn" value="createInitialPopulation"/>
      <!-- Use the next three functions instead of the previous three if you set the
            population type to doubleVector -->
      <!--<Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>-->

          <!-- 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="80"/>
      <!-- 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="power"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rationalpso

Generate Rational models using PSO

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
      <!-- 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="80"/>
      <!-- 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="power"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

polynomialfixed

Build polynomial models with a fixed order

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">

   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#PolynomialFactory|PolynomialFactory]]">
      <!-- Specifies the structure of the polynomial -->
      <!-- Expects matlab matrix: Element (i,j) is the exponent of variable i for term j.  -->
      <Option key="degrees" value="[0 0;1 0;0 1;1 1;2 0;0 2;2 2]"/> <!-- Equals to 1+x+y+xy+xx+yy+xxyy -->
      <!-- Base function for interpolation, one of chebyshev, power, legendre -->
      <Option key="basis" value="power"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[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:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfgenetic

Build Radial Basis Function models using a genetic algorithm

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="15"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
      <Option key="type" value="RBF"/>
      
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="creationFcn" value="createInitialPopulation"/>
      
      <!-- 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:ModelFactory|ModelFactory]]>
</[[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:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[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:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
      <Option key="type" value="DACE"/>
      
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="creationFcn" value="createInitialPopulation"/>
      
      <!-- 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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

daceps

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

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="500"/>
      <Option key="maxFunEvals" value="100"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>

   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

gpps

Build GP models

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="500"/>
      <Option key="maxFunEvals" value="100"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>

   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#GaussianProcessFactory|GaussianProcessFactory]]">
      <Option key="covFunction" value="covSEiso"/>      
      
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

gpgenetic

Build Gaussian Process models

<[[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="doubleVector"/>
   <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:ModelFactory|ModelFactory]] type="[[ModelFactory#GaussianProcessFactory|GaussianProcessFactory]]">
            <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>
            <!--
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <Option key="creationFcn" value="createInitialPopulation"/>
            -->
      
      <Option key="covFunction" value="covSEiso"/>
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

dacepso

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

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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:ModelFactory|ModelFactory]] type="[[ModelFactory#BFFactory|BFFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingsim

Build kriging models using Simulated Annealing The kriging models are implemented using The DACE Toolbox from http://www2.imm.dtu.dk/~hbn/dace

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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#MatlabSimAnnealing|MatlabSimAnnealing]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <!-- See the DACE Toolbox documentaion for possible regression and correlation functions -->
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingps

Build kriging models using pattern search

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>               
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingoptim

Build kriging models using the matlab optimization toolbox

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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#MatlabOptimizer|MatlabOptimizer]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>

   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelFactory|ModelFactory]]>
</[[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"/>
   <!-- If you specify "custom" as the population type you will be evolving models
   and will use the genetic operators defined in the KrigingFactory class -->
   <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:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>
<!--          <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>          -->
      
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingpso

Build kriging models using PSO

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#OptimizerModelBuilder|OptimizerModelBuilder]]" 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:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>               
</[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

krigingnsga

Build kriging models using NSGA-II, requires a multi-output or multi-measure setup

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#ParetoModelBuilder|ParetoModelBuilder]]" combineOutputs="true">
   <Option key="restartStrategy" value="model"/>
   <Option key="populationSize" value="30"/>
   <Option key="maxGenerations" value="30"/>
   <Option key="plotParetoFront" value="false"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </[[Config:ModelFactory|ModelFactory]]>
</[[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="plotOptimSurface" value="false"/>
   <!-- Build 100 random models before restarting -->
   <Option key="runSize" value="100"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly1"/>
      <Option key="correlationFunction" value="corrgauss"/>               
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

blindkriging

Build blind kriging models

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="correlationFunction" value="corrgauss"/>
      
      <Option key="backend" value="blind"/>
   </[[Config:ModelFactory|ModelFactory]]>

</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

kriging

Build kriging models using the maximum likelihood to set the thetas

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#KrigingFactory|KrigingFactory]]">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="correlationFunction" value="correxp"/>
      
      <Option key="backend" value="dace"/>
   </[[Config:ModelFactory|ModelFactory]]>

</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinesgenetic

Build spline models with the genetic modelbuilder

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="intelligent"/>
   <Option key="populationType" value="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:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
      <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>
      <!-- <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/> -->

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

splinessim

Build spline models using the Simulated Annealing modelbuilder

<[[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"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
      <Option key="smoothingBounds" value="0,1"/>      
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinesps

Build spline models using the Pattern Search modelbuilder

<[[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"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
      <Option key="smoothingBounds" value="0,1"/>            
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

splinesoptim

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

<[[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"/>

   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SplineFactory|SplineFactory]]">
      <Option key="smoothingBounds" value="0,1"/>      
   </[[Config:ModelFactory|ModelFactory]]>
   
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

anngenetic

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

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="10"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      <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"/>
      <!--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' or 'earlyStopping'
         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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

annfixed

Fixed ANN model builder, allows you to choose the hidden layer structure manually Thus there is no optimization algorithm involved.

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#AdaptiveModelBuilder|AdaptiveModelBuilder]]" combineOutputs="false">
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
      <Option key="allowedLearningRules" value="trainbr"/>
      <Option key="initialSize" value="3,3"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[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:ModelFactory|ModelFactory]] type="[[ModelFactory#ANNFactory|ANNFactory]]">
      <Option key="allowedLearningRules" value="trainbr,trainlm,trainscg"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

fanngenetic

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

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->     
   <Option key="populationType" value="custom"/>     
   <Option key="populationSize" value="10"/>     
   <Option key="crossoverFraction" value="0.7"/>     
   <Option key="maxGenerations" value="10"/>     
   <Option key="eliteCount" value="1"/>     
   <Option key="stallGenLimit" value="4"/>     
   <Option key="stallTimeLimit" value="Inf"/>     
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#FANNFactory|FANNFactory]]">     
      <Option key="crossoverFcn" value="crossover"/>     
      <Option key="mutationFcn" value="mutation"/>     
      <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:ModelFactory|ModelFactory]]>     
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

nanngenetic

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

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <!-- Re-start strategy for resuming the optimization process between sampling iterations.
        One of 'random','continue','model' and 'intelligent' (Default).  See the docs for more information -->
   <Option key="restartStrategy" value="continue"/>
   <!--See that matlab gads toolbox documentation for more information on the options-->     
   <Option key="populationType" value="custom"/>     
   <Option key="populationSize" value="10"/>     
   <Option key="crossoverFraction" value="0.7"/>     
   <Option key="maxGenerations" value="10"/>     
   <Option key="eliteCount" value="1"/>     
   <Option key="stallGenLimit" value="4"/>     
   <Option key="stallTimeLimit" value="Inf"/>     
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#NANNFactory|NANNFactory]]">     
      <Option key="crossoverFcn" value="crossover"/>     
      <Option key="mutationFcn" value="mutation"/>     
      <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:ModelFactory|ModelFactory]]>     
</[[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"/>
   <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:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>
      <!-- <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/> -->
      
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmps

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

<[[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"/>
   
   <!--See that matlab gads toolbox documentation for more information on the options-->      
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmoptim

Use the matlab optimization toolbox to select LSSVM parameters

<[[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"/>
   <!--See the interface matlab file and the optimization toolbox documentation for more information on the options-->
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>

   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmpso

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

<[[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#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:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmsim

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

<[[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"/>
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[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="100"/>
      <Option key="maxevals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmego

Generate LS-SVM models using EGO

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#EGOModelBuilder|EGOModelBuilder]]" combineOutputs="false">
   <Option key="numIterations" value="10"/>
   <Option key="initPopSize" value="5"/>
   <Option key="restartStrategy" value="continue"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
   
   <[[Config:SampleSelector|SampleSelector]] type="[[SampleSelector#OptimizeCriterion|OptimizeCriterion]]" combineOutputs="false">
      <Option key="criterion" value="gExpectedImprovement"/>
      <Option key="criterion_opts" value="1"/>
      
      <[[Config:Optimizer|Optimizer]] type="[[Optimizer#DirectOptimizer|DirectOptimizer]]">
         <Option key="maxevals" value="1000"/>
         <Option key="maxits" value="300"/>
      </[[Config:Optimizer|Optimizer]]>
      
      <Option key="debug" value="off"/>
   </[[Config:SampleSelector|SampleSelector]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

lssvmrandom

Generate random LSSVM models

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   
   <Option key="runSize" value="20"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[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="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:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>
      <!-- <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>-->
      
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmps

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

<[[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"/>
   
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>

   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmsim

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

<[[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"/>
   <!--See the interface matlab file and the gads toolbox documentation for more information on the options-->      
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmoptim

Use the matlab optimization toolbox to select SVM parameters

<[[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"/>
   <!--See the interface matlab file and the optimization toolbox documentation for more
      information on the options-->
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmpso

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

<[[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#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:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[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="100"/>
      <Option key="maxevals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

svmrandom

Generate random SVMs

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <!-- Plot the optimization surface, visualizes the search through the parameter space (2D only) -->
   <Option key="plotOptimSurface" value="false"/>
   
   <Option key="runSize" value="20"/>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
      <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:ModelFactory|ModelFactory]]>
</[[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="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:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
      <Option key="creationFcn" value="@gacreationuniform"/>
      <Option key="crossoverFcn" value="@crossoverheuristic"/>
      <Option key="mutationFcn" value="@mutationadaptfeasible"/>
    <!--      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/> -->
      
      <!--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="0.0001,30"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="500"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnoptim

Build Radial Basis Function Neural networks using the Matlab Optimization Toolbox

<[[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"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabOptimizer|MatlabOptimizer]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>

   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
      <!--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="0.0001,30"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="500"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnps

Build Radial Basis Function Neural networks using Pattern Search

<[[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"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabPatternSearch|MatlabPatternSearch]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
      <Option key="searchMethod" value="GPSPositiveBasis2N"/>
      <Option key="pollMethod" value="MADSPositiveBasis2N"/>         
   </[[Config:Optimizer|Optimizer]]>

   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
      <!--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="0.0001,30"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="500"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnsim

Build Radial Basis Function Neural networks using Pattern Search

<[[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"/>
   
   <[[Config:Optimizer|Optimizer]] type="[[Optimizer#MatlabSimAnnealing|MatlabSimAnnealing]]">
      <Option key="maxIterations" value="100"/>
      <Option key="maxFunEvals" value="20"/>
   </[[Config:Optimizer|Optimizer]]>
   
   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
      <!--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="0.0001,30"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="500"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>

rbfnnrandom

Build random RBF neural networks

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#RandomModelBuilder|RandomModelBuilder]]" combineOutputs="false">
   <Option key="runSize" value="20"/>

   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RBFNNFactory|RBFNNFactory]]">
      <!--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="0.0001,30"/>
      <!--Maximum number of neurons to use per network-->
      <Option key="maxNeurons" value="500"/>
      <Option key="trainingProgress" value="Inf"/>
   </[[Config:ModelFactory|ModelFactory]]>
</[[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. So this model builder is a way to automatically select the best model type.

<[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]] type="[[AdaptiveModelBuilder#GeneticModelBuilder|GeneticModelBuilder]]" combineOutputs="false">
   <Option key="restartStrategy" value="continue"/>
   <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="4"/>
   <!-- Do we want to prevent any model type going completely extinct -->
   <Option key="extinctionPrevention" value="yes"/>   

   <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#HeterogeneousFactory|HeterogeneousFactory]]">
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutation"/>
      
      <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#EnsembleFactory|EnsembleFactory]]">
         <Option key="crossoverFcn" value="crossover"/>
         <Option key="mutationFcn" value="mutation"/>
         <!-- the maximum ensemble size -->
         <Option key="maxSize" value="4"/>
         <!-- Ensemble members should differ this much percent -->
         <Option key="equalityThreshold" value="0.05"/>
      </[[Config:ModelFactory|ModelFactory]]>

      <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#SVMFactory|SVMFactory]]">
         <Option key="creationFcn" value="createInitialPopulation"/>
         <Option key="crossoverFcn" value="crossover"/>
         <Option key="mutationFcn" value="mutation"/>
         
         <Option key="backend" value="lssvm"/>
         <Option key="kernel" value="rbf"/>
         <Option key="kernelParamBounds" value="-4,4"/>
         <Option key="regParamBounds" value="-5,5"/>
      </[[Config:ModelFactory|ModelFactory]]>
      
      <[[Config:ModelFactory|ModelFactory]] type="[[ModelFactory#RationalFactory|RationalFactory]]">
         <Option key="crossoverFcn" value="crossover"/>
         <Option key="mutationFcn" value="mutation"/>
         <Option key="creationFcn" value="createInitialPopulation"/>
         <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="power"/>
      </[[Config:ModelFactory|ModelFactory]]>

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

         <Option key="crossoverFcn" value="crossover"/>
         <Option key="mutationFcn" value="mutation"/>
         <Option key="creationFcn" value="createInitialPopulation"/>
         
         <[[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:ModelFactory|ModelFactory]]>         
   </[[Config:ModelFactory|ModelFactory]]>
</[[Config:AdaptiveModelBuilder|AdaptiveModelBuilder]]>