CMAEvolutionStrategyOptimizer Class

public class CMAEvolutionStrategyOptimizer : PopulationBasedOptimizer<Population<Candidate>, Candidate>

Public Class CMAEvolutionStrategyOptimizer
	Inherits PopulationBasedOptimizer(Of Population(Of Candidate), Candidate)

public ref class CMAEvolutionStrategyOptimizer : public PopulationBasedOptimizer<Population<Candidate>^, Candidate>

type CMAEvolutionStrategyOptimizer = 
    class
        inherit PopulationBasedOptimizer<Population<Candidate>, Candidate>
    end

Inheritance

Object  →  ManagedIterativeAlgorithm<Vector<Double>, Double, OptimizationSolutionReport<Vector<Double>>>  →  MultidimensionalOptimizer<Vector<Double>, OptimizationSolutionReport<Vector<Double>>>  →  PopulationBasedOptimizer<Vector<Double>, Population<Candidate>, Candidate>  →  PopulationBasedOptimizer<Population<Candidate>, Candidate>  →  CMAEvolutionStrategyOptimizer

CMA-ES is a stochastic, derivative-free optimization algorithm for difficult optimization problems in continuous domains. It adapts the full covariance matrix of a normal search distribution.

Use the CMAEvolutionStrategyOptimizer class to find an extremum of an objective function through population-based evolutionary optimization. The algorithm maintains a population of candidate solutions and creates new candidates through sampling from a multivariate normal distribution.

The method searches without derivatives and can be useful for nonconvex and multimodal objectives. Its stopping tests measure progress and the size of the search population; they do not certify stationarity or global optimality. Tighten geometric and fitness tolerances when more accuracy is needed.

The objective function must be supplied as a multivariate function delegate to the ObjectiveFunction property.

For a bounded search, set the LowerBounds and UpperBounds properties to specify the search space constraints and select BoundaryHandling. Bounds are optional when boundary handling is disabled. The ExtremumType property specifies whether a minimum or a maximum of the objective function is desired.

The following example attempts to find a minimum for the Rastrigin function using the CMA-ES optimizer.

The Rastrigin function is a challenging test problem for global optimization algorithms. The function has many local minima, which makes it tricky for an optimizer to find the global minimum.

Reference