Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES

## 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.

Overview | Package | Class | Use | Tree | Deprecated | Index | Help
PREV PACKAGE NEXT PACKAGE | FRAMES NO FRAMES