Package org.moeaframework.algorithm.single

Class EvolutionStrategy

java.lang.Object

org.moeaframework.algorithm.AbstractAlgorithm

org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

org.moeaframework.algorithm.single.SingleObjectiveEvolutionaryAlgorithm

org.moeaframework.algorithm.single.EvolutionStrategy

All Implemented Interfaces:

Extensible, Algorithm, Configurable, EvolutionaryAlgorithm, Stateful

public class EvolutionStrategy
extends SingleObjectiveEvolutionaryAlgorithm

Single-objective (mu + lambda) evolution strategy (ES) algorithm. In this implementation, mu and lambda are both equal to the initial population size. For example, with an initial population of size 1, this mimics the classic (1 + 1)-ES algorithm. Can only be used with mutation operators with a single parent.

References:

1. Ingo Rechenberg. "Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution." Ph.D. thesis, Fromman-Holzboog, 1971.

Field Summary

Fields inherited from class org.moeaframework.algorithm.single.SingleObjectiveEvolutionaryAlgorithm

comparator

Fields inherited from class org.moeaframework.algorithm.AbstractAlgorithm

initialized, numberOfEvaluations, problem, terminated

Constructor Summary

Constructors

Constructor	Description
EvolutionStrategy(Problem problem)	Constructs a new instance of the evolution strategy (ES) algorithm with default settings.
EvolutionStrategy(Problem problem, int initialPopulationSize, AggregateObjectiveComparator comparator, Initialization initialization, SelfAdaptiveNormalVariation variation)	Constructs a new instance of the evolution strategy (ES) algorithm.

Method Summary

Modifier and Type	Method	Description
SelfAdaptiveNormalVariation	getVariation()	Returns the variation operator currently in use by this algorithm.
void	iterate()	Performs one iteration of the algorithm.
void	setVariation(SelfAdaptiveNormalVariation variation)	Replaces the variation operator to be used by this algorithm.

Methods inherited from class org.moeaframework.algorithm.single.SingleObjectiveEvolutionaryAlgorithm

applyConfiguration, getComparator, getConfiguration, getResult, setComparator, setInitialPopulationSize

Methods inherited from class org.moeaframework.algorithm.AbstractEvolutionaryAlgorithm

getArchive, getInitialization, getInitialPopulationSize, getPopulation, initialize, loadState, saveState, setArchive, setInitialization, setPopulation, setVariation

Methods inherited from class org.moeaframework.algorithm.AbstractAlgorithm

assertNotInitialized, evaluate, getExtensions, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, step, terminate

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface org.moeaframework.core.Algorithm

evaluate, evaluateAll, evaluateAll, getNumberOfEvaluations, getProblem, isInitialized, isTerminated, run, run, step, terminate

Methods inherited from interface org.moeaframework.algorithm.extension.Extensible

addExtension, getExtensions, removeExtension

Constructor Details

EvolutionStrategy

public EvolutionStrategy(Problem problem)

Constructs a new instance of the evolution strategy (ES) algorithm with default settings.

Parameters:

problem - the problem to solve

EvolutionStrategy

public EvolutionStrategy(Problem problem,
 int initialPopulationSize,
 AggregateObjectiveComparator comparator,
 Initialization initialization,
 SelfAdaptiveNormalVariation variation)

Constructs a new instance of the evolution strategy (ES) algorithm.

Parameters:

problem - the problem to solve

initialPopulationSize - the initial population size

comparator - the aggregate objective comparator

initialization - the initialization method

variation - the variation operator

Method Details

iterate

public void iterate()

Description copied from class: AbstractAlgorithm

Performs one iteration of the algorithm. This method should be overridden by implementations to perform each logical iteration of the algorithm.

Specified by:

iterate in class AbstractAlgorithm

getVariation

public SelfAdaptiveNormalVariation getVariation()

Description copied from class: AbstractEvolutionaryAlgorithm

Returns the variation operator currently in use by this algorithm.

Overrides:

getVariation in class AbstractEvolutionaryAlgorithm

Returns:

the variation operator

setVariation

public void setVariation(SelfAdaptiveNormalVariation variation)

Replaces the variation operator to be used by this algorithm.

Parameters:

variation - the variation operator