Package org.jgap.impl

Provides stock implementations of the various sub-components that can be plugged-in or custom implemented.

See:
Description

Class Summary

AveragingCrossoverOperator	The averaging crossover operator randomly selects two Chromosomes from the population and "mates" them by randomly picking a gene and then swapping that gene and all subsequent genes between the two Chromosomes.
BestChromosomesSelector	Implementation of a NaturalSelector that takes the top n chromosomes into the next generation. n can be specified.
BooleanGene	A Gene implementation that supports two possible values (alleles) for each gene: true and false.
CauchyRandomGenerator	Cauchy probability density function (cumulative distribution function)
ChainOfSelectors	Ordered chain of NaturalSelectors.
ChromosomePool	Provides a pooling mechanism for Chromosome instances so that discarded Chromosome instances can be recycled, thus saving memory and the overhead of constructing new ones from scratch each time.
CompositeGene	Ordered container for multiple genes Has the same interface as a single gene and could be used accordingly.
CrossoverOperator	The crossover operator randomly selects two Chromosomes from the population and "mates" them by randomly picking a gene and then swapping that gene and all subsequent genes between the two Chromosomes.
DefaultConfiguration	The DefaultConfiguration class simplifies the JGAP configuration process by providing default configuration values for many of the configuration settings.
DefaultCrossoverRateCalculator	Default implementation of a dynamic CrossoverRateCalculator
DefaultMutationRateCalculator	Default implementation of a mutation rate calculcator
DoubleGene	A Gene implementation that supports a double values for its allele.
FittestPopulationMerger	A implementation of the IPopulationMerger interface that merges two populations as specified based on the fitness function, that is, the n fittest chromosomes are returned in the new population, where n is supplied by parameter.
FixedBinaryGene	A Gene implementation that supports two possible values (alleles, 1 and 0) with a fixed length of alleles NOTE: Since this Gene implementation only supports two different values (1 and 0), there's only a 50% chance that invocation of the setToRandomValue() method will actually change the value of this Gene (if it has a value).
GaussianMutationOperator	This genetic operator performs Gaussian mutation across all genes in a given Chromosome.
GaussianRandomGenerator	Gaussian deviation serving as basis for randomly finding a number.
GreedyCrossover	Greedy crossover can be best explained in the terms of the Traveling Salesman Problem: The algorithm selects the first city of one parent, compares the cities leaving that city in both parents, and chooses the closer one to extend the tour.
IntegerGene	A Gene implementation that supports an integer values for its allele.
MutationOperator	The mutation operator runs through the genes in each of the Chromosomes in the population and mutates them in statistical accordance to the given mutation rate.
NumberGene	Base class for all Genes based on numbers.
Pool	A simple, generic pool class that can be used to pool any kind of object.
ReproductionOperator	The reproduction operator loops through each of the Chromosomes in the population and adds them to the list of candidate chromosomes.
StockRandomGenerator	The stock random generator uses the java.util.Random class to provide a simple implementation of the RandomGenerator interface.
StringGene	A Gene implementation that supports a string for its allele.
SwappingMutationOperator	Swaps the genes instead of mutating them.
ThresholdSelector	Implementation of a NaturalSelector that plays tournaments to determine the chromosomes to be taken to the next generation.
TournamentSelector	Implementation of a NaturalSelector that plays tournaments to determine the chromosomes to be taken to the next generation.
WeightedRouletteSelector	A basic implementation of NaturalSelector that models a roulette wheel.

Package org.jgap.impl Description

Provides stock implementations of the various sub-components that can be plugged-in or custom implemented.