001    /*
002     * Licensed to the Apache Software Foundation (ASF) under one or more
003     * contributor license agreements.  See the NOTICE file distributed with
004     * this work for additional information regarding copyright ownership.
005     * The ASF licenses this file to You under the Apache License, Version 2.0
006     * (the "License"); you may not use this file except in compliance with
007     * the License.  You may obtain a copy of the License at
008     *
009     *      http://www.apache.org/licenses/LICENSE-2.0
010     *
011     * Unless required by applicable law or agreed to in writing, software
012     * distributed under the License is distributed on an "AS IS" BASIS,
013     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014     * See the License for the specific language governing permissions and
015     * limitations under the License.
016     */
017    package org.apache.commons.math.genetics;
018    
019    import static org.junit.Assert.assertTrue;
020    
021    import java.util.ArrayList;
022    import java.util.List;
023    
024    import org.junit.Test;
025    
026    /**
027     * This is also an example of usage.
028     * 
029     * This algorithm does "stochastic sorting" of a sequence 0,...,N.
030     * 
031     */
032    public class GeneticAlgorithmTestPermutations {
033        
034        // parameters for the GA
035        private static final int DIMENSION = 20;    
036        private static final int POPULATION_SIZE = 80; 
037        private static final int NUM_GENERATIONS = 200;
038        private static final double ELITISM_RATE = 0.2;
039        private static final double CROSSOVER_RATE = 1;
040        private static final double MUTATION_RATE = 0.08;
041        private static final int TOURNAMENT_ARITY = 2;
042        
043        // numbers from 0 to N-1
044        private static List<Integer> sequence = new ArrayList<Integer>();
045        static {
046            for (int i=0; i<DIMENSION; i++) {
047                sequence.add(i);
048            }
049        }
050    
051        @Test
052        public void test() {
053            // to test a stochastic algorithm is hard, so this will rather be an usage example
054            
055            // initialize a new genetic algorithm
056            GeneticAlgorithm ga = new GeneticAlgorithm(
057                    new OnePointCrossover<Integer>(),
058                    CROSSOVER_RATE,
059                    new RandomKeyMutation(),
060                    MUTATION_RATE,
061                    new TournamentSelection(TOURNAMENT_ARITY)
062            );
063            
064            // initial population
065            Population initial = randomPopulation();
066            // stopping conditions
067            StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS);
068            
069            // best initial chromosome
070            Chromosome bestInitial = initial.getFittestChromosome();
071            
072            // run the algorithm
073            Population finalPopulation = ga.evolve(initial, stopCond);
074            
075            // best chromosome from the final population
076            Chromosome bestFinal = finalPopulation.getFittestChromosome();
077            
078            // the only thing we can test is whether the final solution is not worse than the initial one
079            // however, for some implementations of GA, this need not be true :)
080            
081            assertTrue(bestFinal.compareTo(bestInitial) > 0);
082            
083            //System.out.println(bestInitial);
084            //System.out.println(bestFinal);
085        }
086        
087        
088        /**
089         * Initializes a random population
090         */
091        private static ElitisticListPopulation randomPopulation() {
092            List<Chromosome> popList = new ArrayList<Chromosome>();
093            for (int i=0; i<POPULATION_SIZE; i++) {
094                Chromosome randChrom = new MinPermutations(RandomKey.randomPermutation(DIMENSION));
095                popList.add(randChrom);
096            }
097            return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE);
098        }
099        
100        /**
101         * Chromosomes representing a permutation of (0,1,2,...,DIMENSION-1).
102         * 
103         * The goal is to sort the sequence.
104         */
105        private static class MinPermutations extends RandomKey<Integer> {
106    
107            public MinPermutations(List<Double> representation) {
108                super(representation);
109            }
110    
111            public double fitness() {
112                int res = 0;
113                List<Integer> decoded = decode(sequence);
114                for (int i=0; i<decoded.size(); i++) {
115                    int value = (Integer) decoded.get(i);
116                    if (value != i) {
117                        // bad position found
118                        res += Math.abs(value - i);
119                    }
120                }
121                // the most fitted chromosome is the one with minimal error
122                // therefore we must return negative value
123                return -res; 
124            }
125    
126            @Override
127            public AbstractListChromosome<Double> newFixedLengthChromosome(List<Double> representation) {
128                return new MinPermutations(representation);
129            }
130        }
131    }