Example usage for org.apache.commons.math3.genetics Chromosome compareTo

Introduction

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

Prototype

public int compareTo(final Chromosome another) 

Source Link

Document

Compares two chromosomes based on their fitness.

Usage

From source file:eu.tsp.sal.WSN.java

/**
 * Algorithm run on a number of sensors = LENGTH
 * /*from w w w . ja  v  a  2s. c o  m*/
 * Population has POPULATION_SIZE individuals (SensorIndividual class)
 * each individual has LENGTH genes (~ sensor)
 * 
 *  
 * 
 */

public static void main(String[] args) {
    /**
     *  initialize a new genetic algorithm with
     *      Crossover policy
     *      CROSSOVER_RATE
     *      Mutation policy
     *      MUTATION_RATE
     *      Selection Policy 
     *      
     */

    SensorGeneticAlgorithm ga = new SensorGeneticAlgorithm(new NPointCrossover(2), CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover)
            new SensorMutation(), MUTATION_RATE, new SensorTournamentSelection(TOURNAMENT_ARITY));

    //assertEquals(0, ga.getGenerationsEvolved());
    //System.out.println(ga.getGenerationsEvolved());

    // initial population of POPULATION_SIZE SensorIndividual
    //Population initial = randomPopulation(LENGTH, POPULATION_SIZE);
    Population initial = randomPopulationWithFixedOA(OA_NUMBER, LENGTH, POPULATION_SIZE);

    printPopulation(initial);

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

    // best initial chromosome
    Chromosome bestInitial = initial.getFittestChromosome();
    System.out.println("Best Individual in initial population (highest fitness) = " + bestInitial);
    System.out.println("Solution  of the best individual = " + ((SensorIndividual) bestInitial).solution());

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

    // best SensorIndividual from the final population
    Chromosome bestFinal = finalPopulation.getFittestChromosome();
    System.out.println("\nBest Individual in final population (highest fitness) = " + bestFinal);
    //System.out.println("Solution of the best individual = " + ((SensorIndividual) bestFinal).solution());
    printSolution((SensorIndividual) bestFinal);

    // Assertion

    // assertTrue(bestFinal.compareTo(bestInitial) > 0);
    // assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());
    System.out.println((bestFinal.compareTo(bestInitial) > 0) ? "Final generation is better than the initial"
            : "Final generation is worse than the initial!!!!");
    //System.out.println(ga.getGenerationsEvolved());
}

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From source file:ga.GeneticAlgorithmTestBinary.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 StatisticGeneticAlgorithm(new OnePointCrossover<Integer>(), CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover)
            new BinaryMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    Assert.assertEquals(0, ga.getGenerationsEvolved());

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

    // the only thing we can test is whether the final solution is not worse than the initial
    // one/*from ww w  .  j  a v a2s . c o m*/
    // however, for some implementations of GA, this need not be true :)

    Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0);
    Assert.assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());
    System.out.println(bestFinal);

}

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From source file:p.lodz.playground.ApacheGeneticsTest.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, // all
            // selected
            // chromosomes
            // will be
            // recombined
            // (=crosssover)
            new BinaryMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY));

    Assert.assertEquals(0, ga.getGenerationsEvolved());

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

    // 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);
    Assert.assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved());

    System.out.println(bestFinal);
}

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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);
}

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