org.uncommons.watchmaker.framework
Interface EvolutionaryOperator<T>

Type Parameters:

T - The type of evolvable entity that this operator accepts.

All Known Implementing Classes:

AbstractCrossover, BitStringCrossover, BitStringMutation, ByteArrayCrossover, CharArrayCrossover, DoubleArrayCrossover, EvolutionPipeline, IdentityOperator, IntArrayCrossover, ListCrossover, ListInversion, ListOperator, ListOrderCrossover, ListOrderMutation, ObjectArrayCrossover, Replacement, SplitEvolution, StringCrossover, StringMutation

public interface EvolutionaryOperator<T>

An evolutionary operator is a function that takes a population of candidates as an argument and returns a new population that is the result of applying a transformation to the original population.

Author:

Daniel Dyer

Method Summary

List<T>

apply(List<T> selectedCandidates, Random rng) Apply the operation to each entry in the list of selected candidates.

Method Detail

apply

List<T> apply(List<T> selectedCandidates,
              Random rng)

Apply the operation to each entry in the list of selected candidates. It is important to note that this method operates on the list of candidates returned by the selection strategy and not on the current population. Each entry in the list (not each individual - the list may contain the same individual more than once) must be operated on exactly once.

Implementing classes should not assume any particular ordering (or lack of ordering) for the selection. If ordering or shuffling is required, it should be performed by the implementing class. The implementation should not re-order the list provided but instead should make a copy of the list and re-order that. The ordering of the selection should be totally irrelevant for operators that process each candidate in isolation, such as mutation. It should only be an issue for operators, such as cross-over, that deal with multiple candidates in a single operation.

The operator should not modify any of the candidates passed in. Instead it should return a list that contains evolved copies of those candidates (umodified candidates can be included in the results without having to be copied).

Parameters:

selectedCandidates - The individuals to evolve.

rng - A source of randomness for stochastic operators (most operators will be stochastic).

Returns:

The evolved individuals.