public class CMAESOptimizer extends MultivariateOptimizer
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 cmaes.m version 3.51.
For more information, please refer to the following links:
| Modifier and Type | Class and Description |
|---|---|
static class |
CMAESOptimizer.PopulationSize
Population size.
|
static class |
CMAESOptimizer.Sigma
Input sigma values.
|
evaluations| Constructor and Description |
|---|
CMAESOptimizer(int maxIterations,
double stopFitness,
boolean isActiveCMA,
int diagonalOnly,
int checkFeasableCount,
RandomGenerator random,
boolean generateStatistics,
ConvergenceChecker<PointValuePair> checker) |
| Modifier and Type | Method and Description |
|---|---|
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.
|
protected void |
parseOptimizationData(OptimizationData... optData)
Scans the list of (required and optional) optimization data that
characterize the problem.
|
computeObjectiveValue, getGoalTypegetLowerBound, getStartPoint, getUpperBoundgetConvergenceChecker, getEvaluations, getIterations, getMaxEvaluations, getMaxIterations, incrementEvaluationCount, incrementIterationCount, optimizepublic CMAESOptimizer(int maxIterations,
double stopFitness,
boolean isActiveCMA,
int diagonalOnly,
int checkFeasableCount,
RandomGenerator random,
boolean generateStatistics,
ConvergenceChecker<PointValuePair> checker)
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.public List<Double> getStatisticsSigmaHistory()
public List<RealMatrix> getStatisticsMeanHistory()
public List<Double> getStatisticsFitnessHistory()
public List<RealMatrix> getStatisticsDHistory()
public PointValuePair optimize(OptimizationData... optData) throws TooManyEvaluationsException, DimensionMismatchException
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).
Important note: Subclasses must override
BaseOptimizer.parseOptimizationData(OptimizationData[]) if they need to register
their own options; but then, they must also call
super.parseOptimizationData(optData) within that method.
optimize in class MultivariateOptimizeroptData - Optimization data. In addition to those documented in
MultivariateOptimizer, this method will register the following data:
TooManyEvaluationsException - if the maximal number of
evaluations is exceeded.DimensionMismatchException - if the initial guess, target, and weight
arguments have inconsistent dimensions.protected PointValuePair doOptimize()
doOptimize in class BaseOptimizer<PointValuePair>protected void parseOptimizationData(OptimizationData... optData)
parseOptimizationData in class MultivariateOptimizeroptData - Optimization data. The following data will be looked for:
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