org.apache.commons.math3.optimization.direct

## Class CMAESOptimizer

- 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 N2 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:
  - Matlab code
  - Introduction to CMA-ES
  - Wikipedia
  Since:
    3.0
  Version:
    $Id: CMAESOptimizer.java 1540165 2013-11-08 19:53:58Z tn $

- - ### 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.

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