/** * Copyright (C) 2010-2017 Gordon Fraser, Andrea Arcuri and EvoSuite * contributors * * This file is part of EvoSuite. * * EvoSuite is free software: you can redistribute it and/or modify it * under the terms of the GNU Lesser General Public License as published * by the Free Software Foundation, either version 3.0 of the License, or * (at your option) any later version. * * EvoSuite is distributed in the hope that it will be useful, but * WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU * Lesser Public License for more details. * * You should have received a copy of the GNU Lesser General Public * License along with EvoSuite. If not, see <http://www.gnu.org/licenses/>. */ package org.evosuite.ga; import java.io.Serializable; import org.slf4j.Logger; import org.slf4j.LoggerFactory; /** * Abstract base class of fitness functions * * @author Gordon Fraser */ public abstract class FitnessFunction<T extends Chromosome> implements Serializable { private static final long serialVersionUID = -8876797554111396910L; /** Constant <code>logger</code> */ protected static final Logger logger = LoggerFactory.getLogger(FitnessFunction.class); /** * Make sure that the individual gets to know about its fitness * * @param individual * a {@link org.evosuite.ga.Chromosome} object. * @param fitness * a double. */ protected void updateIndividual(FitnessFunction<?> ff, T individual, double fitness) { individual.setFitness(ff, fitness); } protected void updateIndividual(FitnessFunction<?> ff, T individual, double fitness, int toCoverTargets) { individual.setFitness(ff, fitness); } /** * If the fitness function as an archive, returns the best individual in the archive. * returns null otherwise * @return */ public T getBestStoredIndividual(){ return null; } /** * Calculate and set fitness function #TODO the 'set fitness' part should be * done by some abstract super class of all FitnessFunctions * * @param individual * a {@link org.evosuite.ga.Chromosome} object. * @return new fitness */ public abstract double getFitness(T individual); /** * Normalize a value using Andrea's normalization function * * @param value * a double. * @return a double. * @throws java.lang.IllegalArgumentException * if any. */ public static double normalize(double value) throws IllegalArgumentException { if (value < 0d) { throw new IllegalArgumentException("Values to normalize cannot be negative"); } if (Double.isInfinite(value)) { return 1.0; } return value / (1.0 + value); } /** * Do we need to maximize, or minimize this function? * * @return a boolean. */ public abstract boolean isMaximizationFunction(); /** * if the fitness function contains an archive, updates the archive and the fitness values of the population, and returns true. * if the fitness function doesn't contain an archive, return false. * * This method has to be invoked after each generation. * @return */ public boolean updateCoveredGoals(){ return false; } }