CMAESOptimizer (Apache Commons Math 3.6.1 API)

java.lang.Object
- org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer<FUNC>
- - org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateSimpleBoundsOptimizer<MultivariateFunction>
  - - org.apache.commons.math3.optimization.direct.CMAESOptimizer

All Implemented Interfaces:

BaseMultivariateOptimizer<MultivariateFunction>, BaseMultivariateSimpleBoundsOptimizer<MultivariateFunction>, BaseOptimizer<PointValuePair>, MultivariateOptimizer

Deprecated.
As of 3.1 (to be removed in 4.0).
```
@Deprecated
public class CMAESOptimizer
extends BaseAbstractMultivariateSimpleBoundsOptimizer<MultivariateFunction>
implements MultivariateOptimizer
```
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 and Description
`static class`	`CMAESOptimizer.PopulationSize` Deprecated. Population size.
`static class`	`CMAESOptimizer.Sigma` Deprecated. Input sigma values.

Field Summary

Fields
Modifier and Type	Field and Description
`static int`	`DEFAULT_CHECKFEASABLECOUNT` Deprecated. Default value for `checkFeasableCount`: 0.
`static int`	`DEFAULT_DIAGONALONLY` Deprecated. Default value for `diagonalOnly`: 0.
`static boolean`	`DEFAULT_ISACTIVECMA` Deprecated. Default value for `isActiveCMA`: true.
`static int`	`DEFAULT_MAXITERATIONS` Deprecated. Default value for `maxIterations`: 30000.
`static RandomGenerator`	`DEFAULT_RANDOMGENERATOR` Deprecated. Default value for `random`.
`static 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 and 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.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

Methods
Modifier and Type	Method and 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 Detail
  - 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 Detail
  - 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.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 Detail
  - 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 Detail

DEFAULT_CHECKFEASABLECOUNT

DEFAULT_STOPFITNESS

DEFAULT_ISACTIVECMA

DEFAULT_MAXITERATIONS

DEFAULT_DIAGONALONLY

DEFAULT_RANDOMGENERATOR

Constructor Detail

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

CMAESOptimizer

Method Detail

getStatisticsSigmaHistory

getStatisticsMeanHistory

getStatisticsFitnessHistory

getStatisticsDHistory

optimizeInternal

doOptimize