Example usage for org.apache.commons.math3.genetics RandomKeyMutation RandomKeyMutation

Introduction

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

Prototype

RandomKeyMutation

Source Link

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 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 > 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);
}

---

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;//from   ww w.j  a  v a2s . c o 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 > 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);
}

---

From source file:tmp.GeneticAlgorithmTestPermutations.java

@Test
public void test() {
    // 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();
    System.out.print("Initial population");
    System.out.println(initial.getFittestChromosome());

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

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

    // run the algorithm
    Population finalPopulation = ga.evolve(initial, stopCond);

    // best chromosome from the final population
    Chromosome bestFinal = finalPopulation.getFittestChromosome();
    System.out.print("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);
}

---