## org.apache.commons.math3.optim.nonlinear.scalar.noderiv Class CMAESOptimizer

java.lang.Object
org.apache.commons.math3.optim.BaseOptimizer<PAIR>
org.apache.commons.math3.optim.BaseMultivariateOptimizer<PointValuePair>
org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer
org.apache.commons.math3.optim.nonlinear.scalar.noderiv.CMAESOptimizer

public class CMAESOptimizer
extends 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.

Since:
3.0
Version:
\$Id: CMAESOptimizer.java 1400108 2012-10-19 14:20:16Z erans \$

Nested Class Summary
static class CMAESOptimizer.PopulationSize
Population size.
static class CMAESOptimizer.Sigma
Input sigma values.

Field Summary

Fields inherited from class org.apache.commons.math3.optim.BaseOptimizer
evaluations

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

Method Summary
protected  PointValuePair doOptimize()
Performs the bulk of the optimization algorithm.
List<RealMatrix> getStatisticsDHistory()

List<Double> getStatisticsFitnessHistory()

List<RealMatrix> getStatisticsMeanHistory()

List<Double> getStatisticsSigmaHistory()

PointValuePair optimize(OptimizationData... optData)
Stores data and performs the optimization.

Methods inherited from class org.apache.commons.math3.optim.nonlinear.scalar.MultivariateOptimizer
computeObjectiveValue, getGoalType

Methods inherited from class org.apache.commons.math3.optim.BaseMultivariateOptimizer
getLowerBound, getStartPoint, getUpperBound

Methods inherited from class org.apache.commons.math3.optim.BaseOptimizer
getConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount

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

Constructor Detail

### CMAESOptimizer

public CMAESOptimizer(int maxIterations,
double stopFitness,
boolean isActiveCMA,
int diagonalOnly,
int checkFeasableCount,
RandomGenerator random,
boolean generateStatistics,
ConvergenceChecker<PointValuePair> checker)
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()
Returns:
History of sigma values.

### getStatisticsMeanHistory

public List<RealMatrix> getStatisticsMeanHistory()
Returns:
History of mean matrix.

### getStatisticsFitnessHistory

public List<Double> getStatisticsFitnessHistory()
Returns:
History of fitness values.

### getStatisticsDHistory

public List<RealMatrix> getStatisticsDHistory()
Returns:
History of D matrix.

### optimize

public PointValuePair optimize(OptimizationData... optData)
throws TooManyEvaluationsException,
DimensionMismatchException
Stores data and performs the optimization.
The list of parameters is open-ended so that sub-classes can extend it with arguments specific to their concrete implementations.
When the method is called multiple times, instance data is overwritten only when actually present in the list of arguments: when not specified, data set in a previous call is retained (and thus is optional in subsequent calls).

Overrides:
optimize in class MultivariateOptimizer
Parameters:
optData - Optimization data. The following data will be looked for:
Returns:
a point/value pair that satifies the convergence criteria.
Throws:
TooManyEvaluationsException - if the maximal number of evaluations is exceeded.
DimensionMismatchException - if the initial guess, target, and weight arguments have inconsistent dimensions.

### doOptimize

protected PointValuePair doOptimize()
Performs the bulk of the optimization algorithm.

Specified by:
doOptimize in class BaseOptimizer<PointValuePair>
Returns:
the point/value pair giving the optimal value of the objective function.