skim-ga

A simple framework for writing genetic algorithms.

Motivation

...

Structure

All the protein, without any of the fat!

Models

Gene = sequence of bases

interface Gene<T>
    void randomize()

    T[] getSequence()
    void setSequence(T[] sequence)

    T getBaseAt(int index)
    void setBaseAt(int index, T base)

    int length()

Chromosome = series of genes

interface Chromosome
    void randomize()

    Gene getGeneAt(int index)
    void setGeneAt(int index, Gene gene)

    Chromosome crossover(Chromosome other)
    void mutate(double probability)

    int length()

Population = collection of chromosomes

interface Population
    void initialize()

    boolean add(Chromosome chromosome)

    int size()
    Iterator<Chromosome> iterator()

Function = arbitrary code that assigns fitness value to chromosome

interface Function
    double evaluate(Chromosome chromosome)

Selector = logic that determines chromosomes used in next population

interface Selector
    Chromosome[] select(int count, Function function, Population population)

Algorithm = process that controls how population evolves

interface Algorithm
    Population evolve(Function function, Selector selector, Population population)

    boolean shouldTerminate()

The Flow

Consult The Project Spot for a more detailed description.

Outline

1. Initialize random population
2. Select chromosomes through fitness evaluation
3. Generate next population via crossover and mutation

Code Stubs

Main

population.initialize();
while (!algorithm.shouldTerminate()) {
    population = algorithm.evolve(function, selector, population);
}

Algorithm

... implements Algorithm {
    ...

    Population evolve(Function function, Selector selector, Population population) {
        Population nextPopulation = ...;

        Chromosome[] chromosomes = selector.select(population.size(), function, population);
        for (int index = 0; index < chromosomes.length; index++) {
            Chromosome chromosome = chromosomes[i].crossover(chromosomes[(i + 1) % chromosomes.length]);
            chromosome.mutate(probability);

            nextPopulation.add(chromosome);
        }

        return nextPopulation;
    }

    ...
}