Class CMAESOptimizer
- java.lang.Object
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- org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer<FUNC>
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- org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateSimpleBoundsOptimizer<MultivariateFunction>
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- org.apache.commons.math3.optimization.direct.CMAESOptimizer
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- 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 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 function evaluations. On difficult problems the complete optimization (a single run) is expected to take roughly between and function evaluations.
This implementation is translated and adapted from the Matlab version of the CMA-ES algorithm as implemented in module
For more information, please refer to the following links:cmaes.m
version 3.51.- Since:
- 3.0
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Nested Class Summary
Nested Classes Modifier and Type Class Description private static class
CMAESOptimizer.DoubleIndex
Deprecated.Used to sort fitness values.private class
CMAESOptimizer.FitnessFunction
Deprecated.Normalizes fitness values to the range [0,1].static class
CMAESOptimizer.PopulationSize
Deprecated.Population size.static class
CMAESOptimizer.Sigma
Deprecated.Input sigma values.
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Field Summary
Fields Modifier and Type Field Description private RealMatrix
B
Deprecated.Coordinate system.private RealMatrix
BD
Deprecated.B*D, stored for efficiency.private RealMatrix
C
Deprecated.Covariance matrix.private double
cc
Deprecated.Cumulation constant.private double
ccov1
Deprecated.Learning rate for rank-one update.private double
ccov1Sep
Deprecated.Learning rate for rank-one update - diagonalOnlyprivate double
ccovmu
Deprecated.Learning rate for rank-mu update'private double
ccovmuSep
Deprecated.Learning rate for rank-mu update - diagonalOnlyprivate int
checkFeasableCount
Deprecated.Determines how often a new random offspring is generated in case it is not feasible / beyond the defined limits, default is 0.private double
chiN
Deprecated.Expectation of ||N(0,I)|| == norm(randn(N,1)).private double
cs
Deprecated.Cumulation constant for step-size.private RealMatrix
D
Deprecated.Scaling.private double
damps
Deprecated.Damping for step-size.static int
DEFAULT_CHECKFEASABLECOUNT
Deprecated.Default value forcheckFeasableCount
: 0.static int
DEFAULT_DIAGONALONLY
Deprecated.Default value fordiagonalOnly
: 0.static boolean
DEFAULT_ISACTIVECMA
Deprecated.Default value forisActiveCMA
: true.static int
DEFAULT_MAXITERATIONS
Deprecated.Default value formaxIterations
: 30000.static RandomGenerator
DEFAULT_RANDOMGENERATOR
Deprecated.Default value forrandom
.static double
DEFAULT_STOPFITNESS
Deprecated.Default value forstopFitness
: 0.0.private RealMatrix
diagC
Deprecated.Diagonal of C, used for diagonalOnly.private RealMatrix
diagD
Deprecated.Diagonal of sqrt(D), stored for efficiency.private int
diagonalOnly
Deprecated.Defines the number of initial iterations, where the covariance matrix remains diagonal and the algorithm has internally linear time complexity.private int
dimension
Deprecated.Number of objective variables/problem dimensionprivate double[]
fitnessHistory
Deprecated.History queue of best values.private boolean
generateStatistics
Deprecated.Indicates whether statistic data is collected.private int
historySize
Deprecated.Size of history queue of best values.private double[]
inputSigma
Deprecated.private boolean
isActiveCMA
Deprecated.Covariance update mechanism, default is active CMA.private boolean
isMinimize
Deprecated.Number of objective variables/problem dimensionprivate int
iterations
Deprecated.Number of iterations already performed.private int
lambda
Deprecated.Population size, offspring number.private double
logMu2
Deprecated.log(mu + 0.5), stored for efficiency.private int
maxIterations
Deprecated.Maximal number of iterations allowed.private int
mu
Deprecated.Number of parents/points for recombination.private double
mueff
Deprecated.Variance-effectiveness of sum w_i x_i.private double
normps
Deprecated.Norm of ps, stored for efficiency.private RealMatrix
pc
Deprecated.Evolution path.private RealMatrix
ps
Deprecated.Evolution path for sigma.private RandomGenerator
random
Deprecated.Random generator.private double
sigma
Deprecated.Overall standard deviation - search volume.private java.util.List<RealMatrix>
statisticsDHistory
Deprecated.History of D matrix.private java.util.List<java.lang.Double>
statisticsFitnessHistory
Deprecated.History of fitness values.private java.util.List<RealMatrix>
statisticsMeanHistory
Deprecated.History of mean matrix.private java.util.List<java.lang.Double>
statisticsSigmaHistory
Deprecated.History of sigma values.private double
stopFitness
Deprecated.Limit for fitness value.private double
stopTolFun
Deprecated.Stop if fun-changes smaller stopTolFun.private double
stopTolHistFun
Deprecated.Stop if back fun-changes smaller stopTolHistFun.private double
stopTolUpX
Deprecated.Stop if x-changes larger stopTolUpX.private double
stopTolX
Deprecated.Stop if x-change smaller stopTolX.private RealMatrix
weights
Deprecated.Array for weighted recombination.private RealMatrix
xmean
Deprecated.Objective variables.-
Fields inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer
evaluations
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Constructor Summary
Constructors Constructor Description CMAESOptimizer()
Deprecated.As of version 3.1: Parameterlambda
must be passed with the call tooptimize
(whereas in the current code it is set to an undocumented value).CMAESOptimizer(int lambda)
Deprecated.As of version 3.1: Parameterlambda
must be passed with the call tooptimize
(whereas in the current code it is set to an undocumented value)..CMAESOptimizer(int lambda, double[] inputSigma)
Deprecated.CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics)
Deprecated.CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)
Deprecated.CMAESOptimizer(int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics, ConvergenceChecker<PointValuePair> checker)
Deprecated.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Deprecated Methods Modifier and Type Method Description private void
checkParameters()
Deprecated.Checks dimensions and values of boundaries and inputSigma if defined.private static void
copyColumn(RealMatrix m1, int col1, RealMatrix m2, int col2)
Deprecated.Copies a column from m1 to m2.private static RealMatrix
diag(RealMatrix m)
Deprecated.private static RealMatrix
divide(RealMatrix m, RealMatrix n)
Deprecated.protected PointValuePair
doOptimize()
Deprecated.Perform the bulk of the optimization algorithm.private static RealMatrix
eye(int n, int m)
Deprecated.java.util.List<RealMatrix>
getStatisticsDHistory()
Deprecated.java.util.List<java.lang.Double>
getStatisticsFitnessHistory()
Deprecated.java.util.List<RealMatrix>
getStatisticsMeanHistory()
Deprecated.java.util.List<java.lang.Double>
getStatisticsSigmaHistory()
Deprecated.private void
initializeCMA(double[] guess)
Deprecated.Initialization of the dynamic search parametersprivate static int[]
inverse(int[] indices)
Deprecated.private static RealMatrix
log(RealMatrix m)
Deprecated.private static double
max(double[] m)
Deprecated.private static double
max(RealMatrix m)
Deprecated.private static double
min(double[] m)
Deprecated.private static double
min(RealMatrix m)
Deprecated.private static RealMatrix
ones(int n, int m)
Deprecated.protected PointValuePair
optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType, OptimizationData... optData)
Deprecated.Optimize an objective function.private void
parseOptimizationData(OptimizationData... optData)
Deprecated.Scans the list of (required and optional) optimization data that characterize the problem.private static void
push(double[] vals, double val)
Deprecated.Pushes the current best fitness value in a history queue.private double[]
randn(int size)
Deprecated.private RealMatrix
randn1(int size, int popSize)
Deprecated.private static RealMatrix
repmat(RealMatrix mat, int n, int m)
Deprecated.private static int[]
reverse(int[] indices)
Deprecated.private static RealMatrix
selectColumns(RealMatrix m, int[] cols)
Deprecated.private static RealMatrix
sequence(double start, double end, double step)
Deprecated.private int[]
sortedIndices(double[] doubles)
Deprecated.Sorts fitness values.private static RealMatrix
sqrt(RealMatrix m)
Deprecated.private static RealMatrix
square(RealMatrix m)
Deprecated.private static RealMatrix
sumRows(RealMatrix m)
Deprecated.private static RealMatrix
times(RealMatrix m, RealMatrix n)
Deprecated.private static RealMatrix
triu(RealMatrix m, int k)
Deprecated.private void
updateBD(double negccov)
Deprecated.Update B and D from C.private void
updateCovariance(boolean hsig, RealMatrix bestArx, RealMatrix arz, int[] arindex, RealMatrix xold)
Deprecated.Update of the covariance matrix C.private void
updateCovarianceDiagonalOnly(boolean hsig, RealMatrix bestArz)
Deprecated.Update of the covariance matrix C for diagonalOnly > 0private boolean
updateEvolutionPaths(RealMatrix zmean, RealMatrix xold)
Deprecated.Update of the evolution paths ps and pc.private static RealMatrix
zeros(int n, int m)
Deprecated.-
Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateSimpleBoundsOptimizer
optimize, optimize
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Methods inherited from class org.apache.commons.math3.optimization.direct.BaseAbstractMultivariateOptimizer
computeObjectiveValue, getConvergenceChecker, getEvaluations, getGoalType, getLowerBound, getMaxEvaluations, getStartPoint, getUpperBound, optimize, optimizeInternal
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Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
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Methods inherited from interface org.apache.commons.math3.optimization.BaseMultivariateOptimizer
optimize
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Methods inherited from interface org.apache.commons.math3.optimization.BaseOptimizer
getConvergenceChecker, getEvaluations, getMaxEvaluations
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Field Detail
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DEFAULT_CHECKFEASABLECOUNT
public static final int DEFAULT_CHECKFEASABLECOUNT
Deprecated.Default value forcheckFeasableCount
: 0.- See Also:
- Constant Field Values
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DEFAULT_STOPFITNESS
public static final double DEFAULT_STOPFITNESS
Deprecated.Default value forstopFitness
: 0.0.- See Also:
- Constant Field Values
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DEFAULT_ISACTIVECMA
public static final boolean DEFAULT_ISACTIVECMA
Deprecated.Default value forisActiveCMA
: true.- See Also:
- Constant Field Values
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DEFAULT_MAXITERATIONS
public static final int DEFAULT_MAXITERATIONS
Deprecated.Default value formaxIterations
: 30000.- See Also:
- Constant Field Values
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DEFAULT_DIAGONALONLY
public static final int DEFAULT_DIAGONALONLY
Deprecated.Default value fordiagonalOnly
: 0.- See Also:
- Constant Field Values
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DEFAULT_RANDOMGENERATOR
public static final RandomGenerator DEFAULT_RANDOMGENERATOR
Deprecated.Default value forrandom
.
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lambda
private int lambda
Deprecated.Population size, offspring number. The primary strategy parameter to play with, which can be increased from its default value. Increasing the population size improves global search properties in exchange to speed. Speed decreases, as a rule, at most linearly with increasing population size. It is advisable to begin with the default small population size.
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isActiveCMA
private boolean isActiveCMA
Deprecated.Covariance update mechanism, default is active CMA. isActiveCMA = true turns on "active CMA" with a negative update of the covariance matrix and checks for positive definiteness. OPTS.CMA.active = 2 does not check for pos. def. and is numerically faster. Active CMA usually speeds up the adaptation.
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checkFeasableCount
private int checkFeasableCount
Deprecated.Determines how often a new random offspring is generated in case it is not feasible / beyond the defined limits, default is 0.
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inputSigma
private double[] inputSigma
Deprecated.- See Also:
CMAESOptimizer.Sigma
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dimension
private int dimension
Deprecated.Number of objective variables/problem dimension
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diagonalOnly
private int diagonalOnly
Deprecated.Defines the number of initial iterations, where the covariance matrix remains diagonal and the algorithm has internally linear time complexity. diagonalOnly = 1 means keeping the covariance matrix always diagonal and this setting also exhibits linear space complexity. This can be particularly useful for dimension > 100.- See Also:
- A Simple Modification in CMA-ES
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isMinimize
private boolean isMinimize
Deprecated.Number of objective variables/problem dimension
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generateStatistics
private boolean generateStatistics
Deprecated.Indicates whether statistic data is collected.
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maxIterations
private int maxIterations
Deprecated.Maximal number of iterations allowed.
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stopFitness
private double stopFitness
Deprecated.Limit for fitness value.
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stopTolUpX
private double stopTolUpX
Deprecated.Stop if x-changes larger stopTolUpX.
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stopTolX
private double stopTolX
Deprecated.Stop if x-change smaller stopTolX.
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stopTolFun
private double stopTolFun
Deprecated.Stop if fun-changes smaller stopTolFun.
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stopTolHistFun
private double stopTolHistFun
Deprecated.Stop if back fun-changes smaller stopTolHistFun.
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mu
private int mu
Deprecated.Number of parents/points for recombination.
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logMu2
private double logMu2
Deprecated.log(mu + 0.5), stored for efficiency.
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weights
private RealMatrix weights
Deprecated.Array for weighted recombination.
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mueff
private double mueff
Deprecated.Variance-effectiveness of sum w_i x_i.
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sigma
private double sigma
Deprecated.Overall standard deviation - search volume.
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cc
private double cc
Deprecated.Cumulation constant.
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cs
private double cs
Deprecated.Cumulation constant for step-size.
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damps
private double damps
Deprecated.Damping for step-size.
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ccov1
private double ccov1
Deprecated.Learning rate for rank-one update.
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ccovmu
private double ccovmu
Deprecated.Learning rate for rank-mu update'
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chiN
private double chiN
Deprecated.Expectation of ||N(0,I)|| == norm(randn(N,1)).
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ccov1Sep
private double ccov1Sep
Deprecated.Learning rate for rank-one update - diagonalOnly
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ccovmuSep
private double ccovmuSep
Deprecated.Learning rate for rank-mu update - diagonalOnly
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xmean
private RealMatrix xmean
Deprecated.Objective variables.
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pc
private RealMatrix pc
Deprecated.Evolution path.
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ps
private RealMatrix ps
Deprecated.Evolution path for sigma.
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normps
private double normps
Deprecated.Norm of ps, stored for efficiency.
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B
private RealMatrix B
Deprecated.Coordinate system.
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D
private RealMatrix D
Deprecated.Scaling.
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BD
private RealMatrix BD
Deprecated.B*D, stored for efficiency.
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diagD
private RealMatrix diagD
Deprecated.Diagonal of sqrt(D), stored for efficiency.
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C
private RealMatrix C
Deprecated.Covariance matrix.
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diagC
private RealMatrix diagC
Deprecated.Diagonal of C, used for diagonalOnly.
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iterations
private int iterations
Deprecated.Number of iterations already performed.
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fitnessHistory
private double[] fitnessHistory
Deprecated.History queue of best values.
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historySize
private int historySize
Deprecated.Size of history queue of best values.
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random
private RandomGenerator random
Deprecated.Random generator.
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statisticsSigmaHistory
private java.util.List<java.lang.Double> statisticsSigmaHistory
Deprecated.History of sigma values.
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statisticsMeanHistory
private java.util.List<RealMatrix> statisticsMeanHistory
Deprecated.History of mean matrix.
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statisticsFitnessHistory
private java.util.List<java.lang.Double> statisticsFitnessHistory
Deprecated.History of fitness values.
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statisticsDHistory
private java.util.List<RealMatrix> statisticsDHistory
Deprecated.History of D matrix.
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Constructor Detail
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CMAESOptimizer
@Deprecated public CMAESOptimizer()
Deprecated.As of version 3.1: Parameterlambda
must be passed with the call tooptimize
(whereas in the current code it is set to an undocumented value).Default constructor, uses default parameters
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CMAESOptimizer
@Deprecated public CMAESOptimizer(int lambda)
Deprecated.As of version 3.1: Parameterlambda
must be passed with the call tooptimize
(whereas in the current code it is set to an undocumented value)..- Parameters:
lambda
- Population size.
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CMAESOptimizer
@Deprecated public CMAESOptimizer(int lambda, double[] inputSigma)
Deprecated.- Parameters:
lambda
- Population size.inputSigma
- Initial standard deviations to sample new points around the initial guess.
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CMAESOptimizer
@Deprecated public CMAESOptimizer(int lambda, double[] inputSigma, int maxIterations, double stopFitness, boolean isActiveCMA, int diagonalOnly, int checkFeasableCount, RandomGenerator random, boolean generateStatistics)
Deprecated.- 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 thanstopFitness
.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.
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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.- 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 thanstopFitness
.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.
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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 thanstopFitness
.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
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Method Detail
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getStatisticsSigmaHistory
public java.util.List<java.lang.Double> getStatisticsSigmaHistory()
Deprecated.- Returns:
- History of sigma values.
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getStatisticsMeanHistory
public java.util.List<RealMatrix> getStatisticsMeanHistory()
Deprecated.- Returns:
- History of mean matrix.
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getStatisticsFitnessHistory
public java.util.List<java.lang.Double> getStatisticsFitnessHistory()
Deprecated.- Returns:
- History of fitness values.
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getStatisticsDHistory
public java.util.List<RealMatrix> getStatisticsDHistory()
Deprecated.- Returns:
- History of D matrix.
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optimizeInternal
protected PointValuePair optimizeInternal(int maxEval, MultivariateFunction f, GoalType goalType, OptimizationData... optData)
Deprecated.Optimize an objective function.- Overrides:
optimizeInternal
in classBaseAbstractMultivariateOptimizer<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:- Returns:
- the point/value pair giving the optimal value for objective function.
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doOptimize
protected PointValuePair doOptimize()
Deprecated.Perform the bulk of the optimization algorithm.- Specified by:
doOptimize
in classBaseAbstractMultivariateOptimizer<MultivariateFunction>
- Returns:
- the point/value pair giving the optimal value of the objective function.
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parseOptimizationData
private void parseOptimizationData(OptimizationData... optData)
Deprecated.Scans the list of (required and optional) optimization data that characterize the problem.- Parameters:
optData
- Optimization data. The following data will be looked for:
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checkParameters
private void checkParameters()
Deprecated.Checks dimensions and values of boundaries and inputSigma if defined.
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initializeCMA
private void initializeCMA(double[] guess)
Deprecated.Initialization of the dynamic search parameters- Parameters:
guess
- Initial guess for the arguments of the fitness function.
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updateEvolutionPaths
private boolean updateEvolutionPaths(RealMatrix zmean, RealMatrix xold)
Deprecated.Update of the evolution paths ps and pc.- Parameters:
zmean
- Weighted row matrix of the gaussian random numbers generating the current offspring.xold
- xmean matrix of the previous generation.- Returns:
- hsig flag indicating a small correction.
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updateCovarianceDiagonalOnly
private void updateCovarianceDiagonalOnly(boolean hsig, RealMatrix bestArz)
Deprecated.Update of the covariance matrix C for diagonalOnly > 0- Parameters:
hsig
- Flag indicating a small correction.bestArz
- Fitness-sorted matrix of the gaussian random values of the current offspring.
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updateCovariance
private void updateCovariance(boolean hsig, RealMatrix bestArx, RealMatrix arz, int[] arindex, RealMatrix xold)
Deprecated.Update of the covariance matrix C.- Parameters:
hsig
- Flag indicating a small correction.bestArx
- Fitness-sorted matrix of the argument vectors producing the current offspring.arz
- Unsorted matrix containing the gaussian random values of the current offspring.arindex
- Indices indicating the fitness-order of the current offspring.xold
- xmean matrix of the previous generation.
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updateBD
private void updateBD(double negccov)
Deprecated.Update B and D from C.- Parameters:
negccov
- Negative covariance factor.
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push
private static void push(double[] vals, double val)
Deprecated.Pushes the current best fitness value in a history queue.- Parameters:
vals
- History queue.val
- Current best fitness value.
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sortedIndices
private int[] sortedIndices(double[] doubles)
Deprecated.Sorts fitness values.- Parameters:
doubles
- Array of values to be sorted.- Returns:
- a sorted array of indices pointing into doubles.
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log
private static RealMatrix log(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix- Returns:
- Matrix representing the element-wise logarithm of m.
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sqrt
private static RealMatrix sqrt(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix.- Returns:
- Matrix representing the element-wise square root of m.
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square
private static RealMatrix square(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix.- Returns:
- Matrix representing the element-wise square of m.
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times
private static RealMatrix times(RealMatrix m, RealMatrix n)
Deprecated.- Parameters:
m
- Input matrix 1.n
- Input matrix 2.- Returns:
- the matrix where the elements of m and n are element-wise multiplied.
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divide
private static RealMatrix divide(RealMatrix m, RealMatrix n)
Deprecated.- Parameters:
m
- Input matrix 1.n
- Input matrix 2.- Returns:
- Matrix where the elements of m and n are element-wise divided.
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selectColumns
private static RealMatrix selectColumns(RealMatrix m, int[] cols)
Deprecated.- Parameters:
m
- Input matrix.cols
- Columns to select.- Returns:
- Matrix representing the selected columns.
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triu
private static RealMatrix triu(RealMatrix m, int k)
Deprecated.- Parameters:
m
- Input matrix.k
- Diagonal position.- Returns:
- Upper triangular part of matrix.
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sumRows
private static RealMatrix sumRows(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix.- Returns:
- Row matrix representing the sums of the rows.
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diag
private static RealMatrix diag(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix.- Returns:
- the diagonal n-by-n matrix if m is a column matrix or the column matrix representing the diagonal if m is a n-by-n matrix.
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copyColumn
private static void copyColumn(RealMatrix m1, int col1, RealMatrix m2, int col2)
Deprecated.Copies a column from m1 to m2.- Parameters:
m1
- Source matrix.col1
- Source column.m2
- Target matrix.col2
- Target column.
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ones
private static RealMatrix ones(int n, int m)
Deprecated.- Parameters:
n
- Number of rows.m
- Number of columns.- Returns:
- n-by-m matrix filled with 1.
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eye
private static RealMatrix eye(int n, int m)
Deprecated.- Parameters:
n
- Number of rows.m
- Number of columns.- Returns:
- n-by-m matrix of 0 values out of diagonal, and 1 values on the diagonal.
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zeros
private static RealMatrix zeros(int n, int m)
Deprecated.- Parameters:
n
- Number of rows.m
- Number of columns.- Returns:
- n-by-m matrix of zero values.
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repmat
private static RealMatrix repmat(RealMatrix mat, int n, int m)
Deprecated.- Parameters:
mat
- Input matrix.n
- Number of row replicates.m
- Number of column replicates.- Returns:
- a matrix which replicates the input matrix in both directions.
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sequence
private static RealMatrix sequence(double start, double end, double step)
Deprecated.- Parameters:
start
- Start value.end
- End value.step
- Step size.- Returns:
- a sequence as column matrix.
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max
private static double max(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix.- Returns:
- the maximum of the matrix element values.
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min
private static double min(RealMatrix m)
Deprecated.- Parameters:
m
- Input matrix.- Returns:
- the minimum of the matrix element values.
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max
private static double max(double[] m)
Deprecated.- Parameters:
m
- Input array.- Returns:
- the maximum of the array values.
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min
private static double min(double[] m)
Deprecated.- Parameters:
m
- Input array.- Returns:
- the minimum of the array values.
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inverse
private static int[] inverse(int[] indices)
Deprecated.- Parameters:
indices
- Input index array.- Returns:
- the inverse of the mapping defined by indices.
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reverse
private static int[] reverse(int[] indices)
Deprecated.- Parameters:
indices
- Input index array.- Returns:
- the indices in inverse order (last is first).
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randn
private double[] randn(int size)
Deprecated.- Parameters:
size
- Length of random array.- Returns:
- an array of Gaussian random numbers.
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randn1
private RealMatrix randn1(int size, int popSize)
Deprecated.- Parameters:
size
- Number of rows.popSize
- Population size.- Returns:
- a 2-dimensional matrix of Gaussian random numbers.
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