Config:ModelBuilder

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Revision as of 16:16, 13 June 2008 by Tdhaene (talk | contribs)

AdaptiveModelBuilder

rational

Build rational models

<AdaptiveModelBuilder type="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"/>
 -->
 
   <ModelInterface type="RationalSequentialInterface">
      <!-- Bounds for the weights of the rational modeler -->
      <Option key="weightBounds" value="1,40"/>
      <!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
      <Option key="percentBounds" value="1,100"/>
      <!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
      <Option key="maxDegrees" value="40"/>
      <!-- When randomizing rational flags, what percentage should be set -->
      <Option key="percentRational" value="70"/>
      <!-- If a variable is named "f" of "frequency" it will be modeled 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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rationalgenetic

Build rational models using a genetic algorithm

<AdaptiveModelBuilder type="GeneticModelBuilder" combineOutputs="false">
   <!--See that matlab gads toolbox documentation for more information on the options-->
   <Option key="restartStrategy" value="continue"/>
   <Option key="populationType" value="custom"/>
   <Option key="populationSize" value="15"/>
   <Option key="crossoverFraction" value="0.7"/>
   <Option key="maxGenerations" value="10"/>
   <Option key="eliteCount" value="1"/>
   <Option key="stallGenLimit" value="4"/>
   <Option key="stallTimeLimit" value="Inf"/>
 
   <ModelInterface type="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 modeler -->
      <Option key="weightBounds" value="1,40"/>
      <!-- Bounds for the percentage of degrees of freedom wrt number of samples -->
      <Option key="percentBounds" value="1,100"/>
      <!-- Regardless of the percentage bounds, never use more than this many degrees of freedom -->
      <Option key="maxDegrees" value="40"/>
      <!-- When randomizing rational flags, what percentage should be set -->
      <Option key="percentRational" value="70"/>
      <!-- If a variable is named "f" of "frequency" 
         it will be modeled 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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

RBF

Build Radial Basis Function models

<AdaptiveModelBuilder type="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"/>
 -->
 
   <ModelInterface type="BFSequentialInterface">
      <Option key="type" value="RBF"/>
 
      <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
      <BasisFunction name="multiquadric" min=".1" max="5" scale="log"/>
      <!--<BasisFunction name="biharmonic"   min=".1"    max="5"    scale="log"/>
 -->
      <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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

RBFgenetic

Build Radial Basis Function models using a genetic algorithm

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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 -->
      <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
      <BasisFunction name="multiquadric" min=".1" max="5" scale="log"/>
      <!-- <BasisFunction name="biharmonic"   min=".1"    max="5"    scale="log"/>
 -->
      <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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

DACE

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

<AdaptiveModelBuilder type="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"/>
 -->
 
   <ModelInterface type="BFSequentialInterface">
      <Option key="type" value="DACE"/>
 
      <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
      <BasisFunction name="multiquadric" min=".1" max="5" scale="log"/>
      <!--<BasisFunction name="biharmonic"   min=".1"    max="5"    scale="log"/>
 -->
      <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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

DACEgenetic

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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 -->
      <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
      <BasisFunction name="multiquadric" min=".1" max="5" scale="log"/>
      <!-- <BasisFunction name="biharmonic"   min=".1"    max="5"    scale="log"/>
 -->
      <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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

DACEps

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="BFOptimizationInterface">
      <Option key="type" value="DACE"/>
 
      <!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
 
      <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
 
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </ModelInterface>
</AdaptiveModelBuilder>

DACEpso

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

<AdaptiveModelBuilder type="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"/>
 
   <Optimizer type="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"/>
   </Optimizer>
 
   <ModelInterface type="BFOptimizationInterface">
      <Option key="type" value="DACE"/>
 
      <!--Option key="multipleBasisFunctionsAllowed" value="false"/-->
 
      <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
 
      <Option key="regression" value="-1,0,1,2"/>
      <Option key="backend" value="AP"/>
   </ModelInterface>
</AdaptiveModelBuilder>

krigingsim

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="KrigingInterface">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </ModelInterface>
</AdaptiveModelBuilder>

krigingps

Build kriging models using pattern search

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="KrigingInterface">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>               
   </ModelInterface>
</AdaptiveModelBuilder>

krigingoptim

Build kriging models using the matlab optimization toolbox

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="KrigingInterface">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>         
   </ModelInterface>
</AdaptiveModelBuilder>

kriginggenetic

Build kriging models using a genetic algorithm

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>         
   </ModelInterface>
</AdaptiveModelBuilder>

krigingpso

Build kriging models using PSO

<AdaptiveModelBuilder type="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"/>
 
   <Optimizer type="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"/>
   </Optimizer>
 
   <ModelInterface type="KrigingInterface">
      <Option key="lowerThetaBound" value="-5"/>
      <Option key="upperThetaBound" value="3"/>
      <Option key="regressionFunction" value="regpoly0"/>
      <Option key="correlationFunction" value="corrgauss"/>               
</ModelInterface>
</AdaptiveModelBuilder>

krigingrandom

Build kriging models randomly

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

splines

Build spline models sequentially

<AdaptiveModelBuilder type="SequentialModelBuilder" combineOutputs="false">
   <Option key="maximumRunLength" value="30"/>
   <Option key="decay" value=".99"/>
   <Option key="historySize" value="15"/>
   <Option key="strategy" value="best"/>
 
   <ModelInterface type="SplineSequentialInterface">
      <Option key="smoothingBounds" value="0,1"/>   
   </ModelInterface>
</AdaptiveModelBuilder>

splinesgenetic

Build spline models with the genetic modelbuilder

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>   
   </ModelInterface>
</AdaptiveModelBuilder>

splinessim

Build spline models using the Simulated Annealing modelbuilder

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="SplineInterface">
      <Option key="smoothingBounds" value="0,1"/>      
   </ModelInterface>
</AdaptiveModelBuilder>

splinesps

Build spline models using the Pattern Search modelbuilder

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="SplineInterface">
      <Option key="smoothingBounds" value="0,1"/>            
   </ModelInterface>
</AdaptiveModelBuilder>

splinesoptim

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="SplineInterface">
      <Option key="smoothingBounds" value="0,1"/>      
   </ModelInterface>
</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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

anngenetic

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

annrandom

Random ANN model builder, usefull as a baseline comparison

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

fanngenetic

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

<AdaptiveModelBuilder type="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"/>     
 
   <ModelInterface type="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"/>     
   </ModelInterface>     
</AdaptiveModelBuilder>

nanngenetic

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

<AdaptiveModelBuilder type="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"/>     
 
   <ModelInterface type="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"/>     
   </ModelInterface>     
</AdaptiveModelBuilder>

lssvmgenetic

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

lssvmps

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="SVMInterface">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </ModelInterface>
</AdaptiveModelBuilder>

lssvmoptim

Use the matlab optimization toolbox to select LSSVM parameters

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="SVMInterface">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </ModelInterface>
</AdaptiveModelBuilder>

lssvmpso

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

<AdaptiveModelBuilder type="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"/>
 
   <Optimizer type="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"/>
   </Optimizer>
 
   <ModelInterface type="SVMInterface">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </ModelInterface>
</AdaptiveModelBuilder>

lssvmsim

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="SVMInterface">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </ModelInterface>
</AdaptiveModelBuilder>

lssvmdirect

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

<AdaptiveModelBuilder type="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"/>
 
   <Optimizer type="DirectOptimizer">
      <Option key="maxits" value="500"/>
      <Option key="maxevals" value="100"/>
   </Optimizer>
 
   <ModelInterface type="SVMInterface">
      <Option key="backend" value="lssvm"/>
      <Option key="kernel" value="rbf"/>
      <Option key="kernelParamBounds" value="-4,4"/>
      <Option key="regParamBounds" value="-5,5"/>
   </ModelInterface>
</AdaptiveModelBuilder>

lssvmrandom

Generate random LSSVM models

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

svmgenetic

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

svmps

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

svmsim

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

svmoptim

Use the matlab optimization toolbox to select SVM parameters

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

svmpso

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

<AdaptiveModelBuilder type="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"/>
 
   <Optimizer type="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"/>
   </Optimizer>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

svmdirect

Use the DIviding RECtangles algorithm to optimize the SVM hyperparameters

<AdaptiveModelBuilder type="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"/>
 
   <Optimizer type="DirectOptimizer">
      <Option key="maxits" value="500"/>
      <Option key="maxevals" value="100"/>
   </Optimizer>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

svmrandom

Generate random SVMs

<AdaptiveModelBuilder type="RandomModelBuilder" combineOutputs="false">
   <Option key="runSize" value="100"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rbfnnbatch

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

<AdaptiveModelBuilder type="BatchModelBuilder" combineOutputs="false">
   <Option key="maxBatches" value="10"/>
   <Option key="maxBatchesNoImprovement" value="3"/>
   <Option key="batchSize" value="10"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rbfnngenetic

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

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rbfnnoptim

Build Radial Basis Function Neural networks using the Matlab Optimization Toolbox

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rbfnnps

Build Radial Basis Function Neural networks using Pattern Search

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rbfnnsim

Build Radial Basis Function Neural networks using Pattern Search

<AdaptiveModelBuilder type="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"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</AdaptiveModelBuilder>

rbfnnrandom

Build random RBF neural networks

<AdaptiveModelBuilder type="RandomModelBuilder" combineOutputs="false">
   <Option key="runSize" value="10"/>
 
   <ModelInterface type="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"/>
   </ModelInterface>
</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.

See : FAQ.

<AdaptiveModelBuilder type="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"/>   
 
   <ModelInterface type="HeterogeneousGeneticInterface">
      <Option key="creationFcn" value="createInitialPopulation"/>
      <Option key="crossoverFcn" value="crossover"/>
      <Option key="mutationFcn" value="mutate"/>
      <Option key="constraintFcn" value="[]"/>
 
      <ModelInterface type="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"/>
      </ModelInterface>
 
      <ModelInterface type="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"/>
      </ModelInterface>
 
      <ModelInterface type="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"/>
      </ModelInterface>
 
      <ModelInterface type="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"/>
 
         <BasisFunction name="gaussian" min=".1" max="5" scale="log"/>
         <BasisFunction name="multiquadric" min=".1" max="5" scale="log"/>
         <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"/>
      </ModelInterface>         
   </ModelInterface>
</AdaptiveModelBuilder>