jcgp.backend.modules.es

Class EvolutionaryStrategy

java.lang.Object

jcgp.backend.modules.Module

jcgp.backend.modules.es.EvolutionaryStrategy

Direct Known Subclasses:

MuPlusLambda, TournamentSelection

public abstract class EvolutionaryStrategy
extends Module

This class specifies the characteristics of an evolutionary strategy. The evolutionary strategy's job is to generate the next population of solutions. In JCGP this is done by modifying the provided population object rather than creating a new one.

A typical implementation of EvolutionaryStratey iterates through the chromosomes in the population and selects the individual(s) to be promoted. It then uses mutator.mutate() to generically mutate the promoted individual(s). Parameter-dependent strategies can be implemented by accessing the parameters via the resources argument.

Parameters may be specified to control the implemented strategy. Any parameters registered with registerParameters() should be displayed by the user interface, if it is being used. See Module for more information.

It is advisable to use Resources.reportln() and Resources.report() to print any relevant information. Note that reportln() and report() are affected by the report interval base parameter. Use Resources.println() and Resources.print() to print information regardless of the current generation. See Resources for more information.

Author:

Eduardo Pedroni

See Also:

Module

Method Summary

Modifier and Type	Method and Description
abstract void	evolve(Population population, Mutator mutator) Performs the selection algorithm and uses the mutator to create the next generation of solutions.

Methods inherited from class jcgp.backend.modules.Module

getLocalParameters, toString

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Method Detail

evolve

public abstract void evolve(Population population,
                            Mutator mutator)

Performs the selection algorithm and uses the mutator to create the next generation of solutions.

Parameters:

population - the population to evolve.

mutator - the mutator with which to mutate the promoted individuals.