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.*; 020 021 import java.util.LinkedList; 022 import java.util.List; 023 import org.junit.Test; 024 025 026 public class FitnessCachingTest { 027 028 // parameters for the GA 029 private static final int DIMENSION = 50; 030 private static final double CROSSOVER_RATE = 1; 031 private static final double MUTATION_RATE = 0.1; 032 private static final int TOURNAMENT_ARITY = 5; 033 034 private static final int POPULATION_SIZE = 10; 035 private static final int NUM_GENERATIONS = 50; 036 private static final double ELITISM_RATE = 0.2; 037 038 // how many times was the fitness computed 039 public static int fitnessCalls = 0; 040 041 042 @Test 043 public void testFitnessCaching() { 044 // initialize a new genetic algorithm 045 GeneticAlgorithm ga = new GeneticAlgorithm( 046 new OnePointCrossover<Integer>(), 047 CROSSOVER_RATE, // all selected chromosomes will be recombined (=crosssover) 048 new BinaryMutation(), 049 MUTATION_RATE, // no mutation 050 new TournamentSelection(TOURNAMENT_ARITY) 051 ); 052 053 // initial population 054 Population initial = randomPopulation(); 055 // stopping conditions 056 StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS); 057 058 // run the algorithm 059 ga.evolve(initial, stopCond); 060 061 int neededCalls = 062 POPULATION_SIZE /*initial population*/ + 063 (NUM_GENERATIONS - 1) /*for each population*/ * (int)(POPULATION_SIZE * (1.0 - ELITISM_RATE)) /*some chromosomes are copied*/ 064 ; 065 assertTrue(fitnessCalls <= neededCalls); // some chromosomes after crossover may be the same os old ones 066 } 067 068 069 /** 070 * Initializes a random population. 071 */ 072 private static ElitisticListPopulation randomPopulation() { 073 List<Chromosome> popList = new LinkedList<Chromosome>(); 074 075 for (int i=0; i<POPULATION_SIZE; i++) { 076 BinaryChromosome randChrom = new DummyCountingBinaryChromosome(BinaryChromosome.randomBinaryRepresentation(DIMENSION)); 077 popList.add(randChrom); 078 } 079 return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE); 080 } 081 082 private static class DummyCountingBinaryChromosome extends DummyBinaryChromosome { 083 084 public DummyCountingBinaryChromosome(List<Integer> representation) { 085 super(representation); 086 } 087 088 @Override 089 public double fitness() { 090 fitnessCalls++; 091 return 0; 092 } 093 } 094 }