| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You 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 org.apache.commons.math4.legacy.genetics; |
| |
| |
| import java.util.ArrayList; |
| import java.util.List; |
| |
| import org.apache.commons.math4.core.jdkmath.JdkMath; |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * This is also an example of usage. |
| * |
| * This algorithm does "stochastic sorting" of a sequence 0,...,N. |
| * |
| */ |
| public class GeneticAlgorithmTestPermutations { |
| |
| // parameters for the GA |
| private static final int DIMENSION = 20; |
| private static final int POPULATION_SIZE = 80; |
| private static final int NUM_GENERATIONS = 200; |
| private static final double ELITISM_RATE = 0.2; |
| private static final double CROSSOVER_RATE = 1; |
| private static final double MUTATION_RATE = 0.08; |
| private static final int TOURNAMENT_ARITY = 2; |
| |
| // numbers from 0 to N-1 |
| private static final List<Integer> sequence = new ArrayList<>(); |
| static { |
| for (int i=0; i<DIMENSION; i++) { |
| sequence.add(i); |
| } |
| } |
| |
| @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(); |
| // 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); |
| |
| //System.out.println(bestInitial); |
| //System.out.println(bestFinal); |
| } |
| |
| |
| /** |
| * Initializes a random population |
| */ |
| private static ElitisticListPopulation randomPopulation() { |
| List<Chromosome> popList = new ArrayList<>(); |
| for (int i=0; i<POPULATION_SIZE; i++) { |
| Chromosome randChrom = new MinPermutations(RandomKey.randomPermutation(DIMENSION)); |
| popList.add(randChrom); |
| } |
| return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE); |
| } |
| |
| /** |
| * Chromosomes representing a permutation of (0,1,2,...,DIMENSION-1). |
| * |
| * The goal is to sort the sequence. |
| */ |
| private static class MinPermutations extends RandomKey<Integer> { |
| |
| MinPermutations(List<Double> representation) { |
| super(representation); |
| } |
| |
| @Override |
| public double fitness() { |
| int res = 0; |
| List<Integer> decoded = decode(sequence); |
| for (int i=0; i<decoded.size(); i++) { |
| int value = decoded.get(i); |
| if (value != i) { |
| // bad position found |
| res += JdkMath.abs(value - i); |
| } |
| } |
| // the most fitted chromosome is the one with minimal error |
| // therefore we must return negative value |
| return -res; |
| } |
| |
| @Override |
| public AbstractListChromosome<Double> newFixedLengthChromosome(List<Double> chromosomeRepresentation) { |
| return new MinPermutations(chromosomeRepresentation); |
| } |
| } |
| } |