de.termininistic.serein.examples.benchmarks.Benchmark.java Source code

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Here is the source code for de.termininistic.serein.examples.benchmarks.Benchmark.java

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/**
 * Copyright (C) 2015 Tobias Uhlig (tobias.uhlig@unibw.de)
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package de.termininistic.serein.examples.benchmarks;

import java.util.Random;

import org.apache.commons.math3.linear.RealVector;

import de.termininistic.serein.examples.benchmarks.functions.BenchmarkFunction;
import de.termininistic.serein.examples.benchmarks.functions.multimodal.RastriginFunction;
import de.terministic.serein.api.EvolutionEnvironment;
import de.terministic.serein.api.Mutation;
import de.terministic.serein.api.Population;
import de.terministic.serein.api.Recombination;
import de.terministic.serein.api.TerminationCondition;
import de.terministic.serein.core.AlgorithmFactory;
import de.terministic.serein.core.BasicIndividual;
import de.terministic.serein.core.Populations;
import de.terministic.serein.core.StatsListener;
import de.terministic.serein.core.genome.DoubleGenome;
import de.terministic.serein.core.genome.mutation.SinglePointMutation;
import de.terministic.serein.core.genome.recombination.UniformCrossover;
import de.terministic.serein.core.genome.translators.LinearTranslator;
import de.terministic.serein.core.selection.individual.TournamentSelection;
import de.terministic.serein.core.termination.TerminationConditionFitness;

public class Benchmark {

    /**
     * @param args
     */
    public static void main(String[] args) {

        // Benchmark parameters
        BenchmarkFunction function = new RastriginFunction();
        int dimension = 10;
        double optimum = function.map(function.getOptimum(dimension));
        double absoluteError = 1.0e-2;

        // Metaheuristic Parameters
        Random random = new Random(1233);
        int populationSize = 100;
        Recombination<DoubleGenome> recombination = new UniformCrossover<>();
        Mutation<DoubleGenome> mutation = new SinglePointMutation<>();
        double recombinationProbability = 1.0;
        BenchmarkFunctionFitness fitness = new BenchmarkFunctionFitness(function);
        TerminationCondition<RealVector> termination = new TerminationConditionFitness<>(fitness,
                optimum + absoluteError);

        // Translator doubleGenome --> function domain
        LinearTranslator translator = new LinearTranslator(function.getDomain().getMinVector(dimension),
                function.getDomain().getMaxVector(dimension));

        // Initial individual 
        DoubleGenome initialGenome = DoubleGenome.createRandomDoubleGenome(dimension, random);
        BasicIndividual<RealVector, DoubleGenome> initialIndividual = new BasicIndividual<RealVector, DoubleGenome>(
                initialGenome, translator);
        initialIndividual.setRecombination(recombination);
        initialIndividual.<Double>setProperty("ProbabilityOfRecombination", recombinationProbability, true);
        initialIndividual.setMutation(mutation);
        initialIndividual.setMateSelection(new TournamentSelection<>(fitness, 3));

        // Start population
        Population<RealVector> startPop = Populations.generatePopulation(initialIndividual, populationSize, random);

        // Assemble Metaheuristic 
        AlgorithmFactory<RealVector> factory = new AlgorithmFactory<>();
        factory.termination = termination;
        EvolutionEnvironment<RealVector> algo = factory.createReferenceEvolutionaryAlgorithm(fitness, startPop,
                random);

        // Add a simple listener (output to console)
        StatsListener<RealVector> listener = new StatsListener<RealVector>(fitness, 50);
        algo.addListener(listener);

        // Start optimization
        algo.evolve();

    }

}