Package org.biojavax.ga.functions

GA functions

See:
Description

Interface Summary

CrossOverFunction	Crosses two chromosomes.
FitnessFunction	Calculates the fitness of an Organism in a Population of Organisms
GACross	Holds the results of a CrossOver event, objects of this type are made by CrossOverFunctions
GACrossResult	Holds the results of a CrossOver event, objects of this type are made by CrossOverFunctions
MutationFunction	A class that mutates a SymbolList
SelectionFunction	Selects Organisms for Replication and returns the offspring.

Class Summary

AbstractCrossOverFunction	Abstract implementation of CrossOverFunction.
AbstractMutationFunction	Abstract implementation of MutationFunction all custom implementations should inherit from here.
CrossOverFunction.NoCross	A place holder CrossOverFunction that doesn't perform cross overs
MutationFunction.NoMutation	Place Holder class that doesn't mutate its SymbolLists
OrderCrossover	This does a 2-point-crossover on two chromosomes keeping the Symbols in each chromosome constant.
ProportionalSelection	A Selection function that determines the proportion of individuals in a new population proportionally to their fitness.
SelectionFunction.SelectAll
SelectionFunction.Threshold	Selects individuals who's fitness exceeds a threshold value.
SimpleCrossOverFunction	Simple Implementation of the CrossOverFunction interface
SimpleGACrossResult	Simple implementation of the GACross interface.
SimpleMutationFunction	Simple no frills Implementation of the MutationFunction interface
SwapMutationFunction	This class does a sort of mutation by exchanging two positions on the chromosome.
TournamentSelection	Tournament Selection chooses the best organisms from n random subsets of a given population.

Package org.biojavax.ga.functions Description

GA functions

A genetic algorithm requires a number of functions. This package provides the interfaces for those functions and simple implementations. By implementing, mixing and matching these functions you can create highly customized genetic algorithms.

A GA requires (in alphabetical order): a CrossOverFunction to govern the behaivour of 'chromosome' crossovers, a FitnessFunction to determine the fitness of each organism after each iteration, a MutationFuntion to govern mutation behaivour, and a SelectionFunction to select organisms for the next round of replication.