org.apache.commons.math3.optimization.direct.CMAESOptimizer

All Implemented Interfaces:: BaseMultivariateOptimizer<MultivariateFunction>, BaseMultivariateSimpleBoundsOptimizer<MultivariateFunction>, BaseOptimizer<PointValuePair>, MultivariateOptimizer

@Deprecated public class CMAESOptimizer extends BaseAbstractMultivariateSimpleBoundsOptimizer<MultivariateFunction> implements MultivariateOptimizer

Deprecated.

As of 3.1 (to be removed in 4.0).

An implementation of the active Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for non-linear, non-convex, non-smooth, global function minimization. The CMA-Evolution Strategy (CMA-ES) is a reliable stochastic optimization method which should be applied if derivative-based methods, e.g. quasi-Newton BFGS or conjugate gradient, fail due to a rugged search landscape (e.g. noise, local optima, outlier, etc.) of the objective function. Like a quasi-Newton method, the CMA-ES learns and applies a variable metric on the underlying search space. Unlike a quasi-Newton method, the CMA-ES neither estimates nor uses gradients, making it considerably more reliable in terms of finding a good, or even close to optimal, solution.

In general, on smooth objective functions the CMA-ES is roughly ten times slower than BFGS (counting objective function evaluations, no gradients provided). For up to $N=10$ variables also the derivative-free simplex direct search method (Nelder and Mead) can be faster, but it is far less reliable than CMA-ES.

The CMA-ES is particularly well suited for non-separable and/or badly conditioned problems. To observe the advantage of CMA compared to a conventional evolution strategy, it will usually take about $30 N$ function evaluations. On difficult problems the complete optimization (a single run) is expected to take roughly between $30 N$ and $300 N$ ² function evaluations.

This implementation is translated and adapted from the Matlab version of the CMA-ES algorithm as implemented in module cmaes.m version 3.51.

For more information, please refer to the following links:

Since:: 3.0

Nested Class Summary

Nested Classes

Modifier and Type

Class

Description

static class

CMAESOptimizer.PopulationSize

Deprecated.

Population size.

static class

CMAESOptimizer.Sigma

Deprecated.

Input sigma values.
Field Summary

Fields

Modifier and Type

Field

Description

static final int

DEFAULT_CHECKFEASABLECOUNT

Deprecated.

Default value for checkFeasableCount: 0.

static final int

DEFAULT_DIAGONALONLY

Deprecated.

Default value for diagonalOnly: 0.

static final boolean

DEFAULT_ISACTIVECMA

Deprecated.

Default value for isActiveCMA: true.

static final int

DEFAULT_MAXITERATIONS

Deprecated.

Default value for maxIterations: 30000.

static final RandomGenerator

DEFAULT_RANDOMGENERATOR

Deprecated.

Default value for random.

static final double

DEFAULT_STOPFITNESS

Deprecated.

Default value for stopFitness: 0.0.

Fields inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer
evaluations
Constructor Summary

Constructors

Constructor

Description

CMAESOptimizer()

Deprecated.
As of version 3.1: Parameter lambda must be passed with the call to optimize (whereas in the current code it is set to an undocumented value).

CMAESOptimizer(int lambda)

Deprecated.
As of version 3.1: Parameter lambda must be passed with the call to optimize (whereas in the current code it is set to an undocumented value)..

CMAESOptimizer(int lambda, double[] inputSigma)

Deprecated.
As of version 3.1: Parameters lambda and inputSigma must be passed with the call to optimize.

CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics)

Deprecated.
See SimpleValueChecker()

CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)

Deprecated.
As of version 3.1: Parameters lambda and inputSigma must be passed with the call to optimize.

CMAESOptimizer(int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)

Deprecated.
Method Summary

Modifier and Type

Method

Description

protected PointValuePair

doOptimize()

Deprecated.

Perform the bulk of the optimization algorithm.

List<RealMatrix>

getStatisticsDHistory()

Deprecated.

List<Double>

getStatisticsFitnessHistory()

Deprecated.

List<RealMatrix>

getStatisticsMeanHistory()

Deprecated.

List<Double>

getStatisticsSigmaHistory()

Deprecated.

protected PointValuePair

optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType, OptimizationData... optData)

Deprecated.

Optimize an objective function.

Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateSimpleBoundsOptimizer
optimize, optimize

Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer
computeObjectiveValue, getConvergenceChecker, getEvaluations, getGoalType, getLowerBound, getMaxEvaluations, getStartPoint, getUpperBound, optimize, optimizeInternal

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.apache.commons.math3.optimization.BaseMultivariateOptimizer
optimize

Methods inherited from interface org.apache.commons.math3.optimization.BaseOptimizer
getConvergenceChecker, getEvaluations, getMaxEvaluations

Field Details
- DEFAULT_CHECKFEASABLECOUNT
  
  public static final int DEFAULT_CHECKFEASABLECOUNT
  
  Deprecated.
  
  Default value for checkFeasableCount: 0.
  See Also:
  
  Constant Field Values
- DEFAULT_STOPFITNESS
  
  public static final double DEFAULT_STOPFITNESS
  
  Deprecated.
  
  Default value for stopFitness: 0.0.
  See Also:
  
  Constant Field Values
- DEFAULT_ISACTIVECMA
  
  public static final boolean DEFAULT_ISACTIVECMA
  
  Deprecated.
  
  Default value for isActiveCMA: true.
  See Also:
  
  Constant Field Values
- DEFAULT_MAXITERATIONS
  
  public static final int DEFAULT_MAXITERATIONS
  
  Deprecated.
  
  Default value for maxIterations: 30000.
  See Also:
  
  Constant Field Values
- DEFAULT_DIAGONALONLY
  
  public static final int DEFAULT_DIAGONALONLY
  
  Deprecated.
  
  Default value for diagonalOnly: 0.
  See Also:
  
  Constant Field Values
- DEFAULT_RANDOMGENERATOR
  
  public static final RandomGenerator DEFAULT_RANDOMGENERATOR
  
  Deprecated.
  
  Default value for random.
Constructor Details
- CMAESOptimizer
  
  @Deprecated public CMAESOptimizer()
  
  Deprecated.
  As of version 3.1: Parameter lambda must be passed with the call to optimize (whereas in the current code it is set to an undocumented value).
  
  Default constructor, uses default parameters
- CMAESOptimizer
  
  @Deprecated public CMAESOptimizer(int lambda)
  
  Deprecated.
  As of version 3.1: Parameter lambda must be passed with the call to optimize (whereas in the current code it is set to an undocumented value)..
  
  Parameters:
  
  lambda - Population size.
- CMAESOptimizer
  
  @Deprecated public CMAESOptimizer(int lambda, double[] inputSigma)
  
  Deprecated.
  As of version 3.1: Parameters lambda and inputSigma must be passed with the call to optimize.
  
  Parameters:
  
  lambda - Population size.
  
  inputSigma - Initial standard deviations to sample new points around the initial guess.
- CMAESOptimizer
  
  @Deprecated public CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics)
  
  Deprecated.
  See SimpleValueChecker()
  
  Parameters:
  
  lambda - Population size.
  
  inputSigma - Initial standard deviations to sample new points around the initial guess.
  
  maxIterations - Maximal number of iterations.
  
  stopFitness - Whether to stop if objective function value is smaller than stopFitness.
  
  isActiveCMA - Chooses the covariance matrix update method.
  
  diagonalOnly - Number of initial iterations, where the covariance matrix remains diagonal.
  
  checkFeasableCount - Determines how often new random objective variables are generated in case they are out of bounds.
  
  random - Random generator.
  
  generateStatistics - Whether statistic data is collected.
- CMAESOptimizer
  
  @Deprecated public CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)
  
  Deprecated.
  As of version 3.1: Parameters lambda and inputSigma must be passed with the call to optimize.
  
  Parameters:
  
  lambda - Population size.
  
  inputSigma - Initial standard deviations to sample new points around the initial guess.
  
  maxIterations - Maximal number of iterations.
  
  stopFitness - Whether to stop if objective function value is smaller than stopFitness.
  
  isActiveCMA - Chooses the covariance matrix update method.
  
  diagonalOnly - Number of initial iterations, where the covariance matrix remains diagonal.
  
  checkFeasableCount - Determines how often new random objective variables are generated in case they are out of bounds.
  
  random - Random generator.
  
  generateStatistics - Whether statistic data is collected.
  
  checker - Convergence checker.
- CMAESOptimizer
  
  public CMAESOptimizer(int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)
  
  Deprecated.
  
  Parameters:
  
  maxIterations - Maximal number of iterations.
  
  stopFitness - Whether to stop if objective function value is smaller than stopFitness.
  
  isActiveCMA - Chooses the covariance matrix update method.
  
  diagonalOnly - Number of initial iterations, where the covariance matrix remains diagonal.
  
  checkFeasableCount - Determines how often new random objective variables are generated in case they are out of bounds.
  
  random - Random generator.
  
  generateStatistics - Whether statistic data is collected.
  
  checker - Convergence checker.
  
  Since:
  
  3.1
Method Details
- getStatisticsSigmaHistory
  
  public List<Double> getStatisticsSigmaHistory()
  
  Deprecated.
  
  Returns:
  
  History of sigma values.
- getStatisticsMeanHistory
  
  public List<RealMatrix> getStatisticsMeanHistory()
  
  Deprecated.
  
  Returns:
  
  History of mean matrix.
- getStatisticsFitnessHistory
  
  public List<Double> getStatisticsFitnessHistory()
  
  Deprecated.
  
  Returns:
  
  History of fitness values.
- getStatisticsDHistory
  
  public List<RealMatrix> getStatisticsDHistory()
  
  Deprecated.
  
  Returns:
  
  History of D matrix.
- optimizeInternal
  
  protected PointValuePair optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType, OptimizationData... optData)
  
  Deprecated.
  
  Optimize an objective function.
  Overrides:
  
  optimizeInternal in class BaseAbstractMultivariateOptimizer<MultivariateFunction>
  
  Parameters:
  
  maxEval - Allowed number of evaluations of the objective function.
  
  f - Objective function.
  
  goalType - Optimization type.
  
  optData - Optimization data. The following data will be looked for:
  
  InitialGuess
  
  CMAESOptimizer.Sigma
  
  CMAESOptimizer.PopulationSize
  
  Returns:
  
  the point/value pair giving the optimal value for objective function.
- doOptimize
  
  protected PointValuePair doOptimize()
  
  Deprecated.
  
  Perform the bulk of the optimization algorithm.
  
  Specified by:
  
  doOptimize in class BaseAbstractMultivariateOptimizer<MultivariateFunction>
  
  Returns:
  
  the point/value pair giving the optimal value of the objective function.

Class CMAESOptimizer

Nested Class Summary

Field Summary

Fields inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer

Constructor Summary

Method Summary

Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateSimpleBoundsOptimizer

Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer

Methods inherited from class java.lang.Object

Methods inherited from interface org.apache.commons.math3.optimization.BaseMultivariateOptimizer

Methods inherited from interface org.apache.commons.math3.optimization.BaseOptimizer

Field Details

DEFAULT_CHECKFEASABLECOUNT

DEFAULT_STOPFITNESS

DEFAULT_ISACTIVECMA

DEFAULT_MAXITERATIONS

DEFAULT_DIAGONALONLY

DEFAULT_RANDOMGENERATOR

Constructor Details

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

Method Details

getStatisticsSigmaHistory

getStatisticsMeanHistory

getStatisticsFitnessHistory

getStatisticsDHistory

optimizeInternal

doOptimize