Example usage for org.apache.commons.math3.genetics GeneticAlgorithm nextGeneration

Introduction

In this page you can find the example usage for org.apache.commons.math3.genetics GeneticAlgorithm nextGeneration.

Prototype

public Population nextGeneration(final Population current) 

Source Link

Document

Evolve the given population into the next generation.

Usage

From source file:tmp.GACombGraphMoore.java

public static void main(String... args) {
    // to test a stochastic algorithm is hard, so this will rather be an usage example

    // initialize a new genetic algorithm   
    GeneticAlgorithm ga = new GeneticAlgorithm(new OnePointCrossover<Integer>(), CROSSOVER_RATE,
            new RandomKeyMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    // initial population
    Population initial = randomPopulation(args);

    System.out.println("Initial population:");
    System.out.println(initial.getFittestChromosome());
    long lastime = System.currentTimeMillis();

    // stopping conditions
    StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
    // best initial chromosome
    Chromosome bestInitial = initial.getFittestChromosome();

    // run the algorithm
    //        Population finalPopulation = ga.evolve(initial, stopCond);
    double bestfit = initial.getFittestChromosome().fitness();
    Population current = initial;//w w  w.j  a va2s  .  co  m
    int generationsEvolved = 0;
    //        while (!stopCond.isSatisfied(current)) {
    while (bestfit != 0.0) {
        current = ga.nextGeneration(current);
        generationsEvolved++;
        Chromosome bestFinal = current.getFittestChromosome();
        //            System.out.print(bestFinal);
        double atualfit = bestFinal.getFitness();
        if (atualfit > bestfit || System.currentTimeMillis() - lastime > HOUR) {
            bestfit = atualfit;
            //                String strbest = generationsEvolved + "-f=" + atualfit + "-" + ((MinPermutations) bestFinal).decode(sequence).toString().replaceAll(" ", "") + "\n";
            String strbest = generationsEvolved + "-f=" + atualfit;
            //                UtilTmp.dumpString(strbest);
            System.out.println(strbest);
            //                strbest = bestFinal.toString();
            //                UtilTmp.dumpString(strbest);
            System.out.println(strbest);
            //                System.out.println();
            lastime = System.currentTimeMillis();
        }
    }

    // best chromosome from the final population
    Chromosome bestFinal = current.getFittestChromosome();
    System.out.println("Best initial:");
    System.out.println(bestInitial);
    System.out.println(((MinPermutations) bestInitial).decode(sequence));
    System.out.println("Best result:");
    System.out.println(bestFinal);
    System.out.println(((MinPermutations) bestFinal).decode(sequence));

    // the only thing we can test is whether the final solution is not worse than the initial one
    // however, for some implementations of GA, this need not be true :)
    //        Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0);
    //System.out.println(bestInitial);
    //System.out.println(bestFinal);
}

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From source file:tmp.GACombPermutation.java

public static void main(String... args) {
    // to test a stochastic algorithm is hard, so this will rather be an usage example

    // initialize a new genetic algorithm
    GeneticAlgorithm ga = new GeneticAlgorithm(new OnePointCrossover<Integer>(), CROSSOVER_RATE,
            new RandomKeyMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    // initial population
    Population initial = randomPopulation(args);
    System.out.print("Graph e-");
    System.out.print(graph.getEdgeCount());
    System.out.print(" Subgraph e-");
    System.out.println(subgraph.getEdgeCount());

    System.out.println("Initial population:");
    System.out.println(initial.getFittestChromosome());
    long lastime = System.currentTimeMillis();

    // stopping conditions
    StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);

    // best initial chromosome
    Chromosome bestInitial = initial.getFittestChromosome();

    // run the algorithm
    //        Population finalPopulation = ga.evolve(initial, stopCond);
    double bestfit = initial.getFittestChromosome().fitness();
    Population current = initial;// ww w . j  av  a  2  s  . com
    int generationsEvolved = 0;
    //        while (!stopCond.isSatisfied(current)) {
    while (bestfit != 0.0) {
        current = ga.nextGeneration(current);
        generationsEvolved++;
        Chromosome bestFinal = current.getFittestChromosome();
        //            System.out.print(bestFinal);
        double atualfit = bestFinal.getFitness();
        if (atualfit > bestfit || System.currentTimeMillis() - lastime > UtilTmp.ALERT_HOUR) {
            lastime = System.currentTimeMillis();
            System.out.print(generationsEvolved);
            System.out.print("-");
            bestfit = atualfit;
            String strbest = bestFinal.toString() + "\n";
            UtilTmp.dumpString(strbest);
            System.out.print(strbest);
            System.out.println();
        }
    }

    // best chromosome from the final population
    Chromosome bestFinal = current.getFittestChromosome();
    System.out.println("Best result:");
    System.out.println(bestFinal);

    // the only thing we can test is whether the final solution is not worse than the initial one
    // however, for some implementations of GA, this need not be true :)
    //        Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0);
    //System.out.println(bestInitial);
    //System.out.println(bestFinal);
}

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