/*
* This file is part of JGAP.
*
* JGAP offers a dual license model containing the LGPL as well as the MPL.
*
* For licensing information please see the file license.txt included with JGAP
* or have a look at the top of class org.jgap.Chromosome which representatively
* includes the JGAP license policy applicable for any file delivered with JGAP.
*/
package examples.dynamicMutation;
import org.jgap.*;
/**
* Sample fitness function for the DynamicMutation example.
*
* @author Klaus Meffert
* @since 2.6
*/
public class DynamicMutationFitnessFunction
extends FitnessFunction {
/** String containing the CVS revision. Read out via reflection!*/
private final static String CVS_REVISION = "$Revision: 1.4 $";
private final int m_targetAmount;
public static final int MAX_BOUND = 4000;
public DynamicMutationFitnessFunction(int a_targetAmount) {
if (a_targetAmount < 1 || a_targetAmount >= MAX_BOUND) {
throw new IllegalArgumentException(
"Change amount must be between 1 and " + MAX_BOUND + " cents.");
}
m_targetAmount = a_targetAmount;
}
/**
* Determine the fitness of the given Chromosome instance. The higher the
* return value, the more fit the instance. This method should always
* return the same fitness value for two equivalent Chromosome instances.
*
* @param a_subject the Chromosome instance to evaluate
*
* @return positive double reflecting the fitness rating of the given
* Chromosome
* @since 2.0 (until 1.1: return type int)
* @author Neil Rotstan, Klaus Meffert, John Serri
*/
public double evaluate(IChromosome a_subject) {
// Take care of the fitness evaluator. It could either be weighting higher
// fitness values higher (e.g.DefaultFitnessEvaluator). Or it could weight
// lower fitness values higher, because the fitness value is seen as a
// defect rate (e.g. DeltaFitnessEvaluator)
boolean defaultComparation = a_subject.getConfiguration().
getFitnessEvaluator().isFitter(2, 1);
// The fitness value measures both how close the value is to the
// target amount supplied by the user and the total number of coins
// represented by the solution. We do this in two steps: first,
// we consider only the represented amount of change vs. the target
// amount of change and return higher fitness values for amounts
// closer to the target, and lower fitness values for amounts further
// away from the target. Then we go to step 2, which returns a higher
// fitness value for solutions representing fewer total coins, and
// lower fitness values for solutions representing more total coins.
// ------------------------------------------------------------------
int changeAmount = amountOfChange(a_subject);
int totalCoins = getTotalNumberOfCoins(a_subject);
int changeDifference = Math.abs(m_targetAmount - changeAmount);
double fitness;
if (defaultComparation) {
fitness = 0.0d;
}
else {
fitness = MAX_BOUND/2;
}
// Step 1: Determine distance of amount represented by solution from
// the target amount. If the change difference is greater than zero we
// will divide one by the difference in change between the
// solution amount and the target amount. That will give the desired
// effect of returning higher values for amounts closer to the target
// amount and lower values for amounts further away from the target
// amount.
// In the case where the change difference is zero it means that we have
// the correct amount and we assign a higher fitness value.
// ---------------------------------------------------------------------
if (defaultComparation) {
fitness += changeDifferenceBonus(MAX_BOUND/2, changeDifference);
}
else {
fitness -= changeDifferenceBonus(MAX_BOUND/2, changeDifference);
}
// Step 2: We divide the fitness value by a penalty based on the number of
// coins. The higher the number of coins the higher the penalty and the
// smaller the fitness value.
// And inversely the smaller number of coins in the solution the higher
// the resulting fitness value.
// -----------------------------------------------------------------------
if (defaultComparation) {
fitness -= computeCoinNumberPenalty(MAX_BOUND/2, totalCoins);
}
else {
fitness += computeCoinNumberPenalty(MAX_BOUND/2, totalCoins);
}
// Make sure fitness value is always positive.
// -------------------------------------------
return Math.max(1.0d, fitness);
}
/**
* Bonus calculation of fitness value.
* @param a_maxFitness maximum fitness value appliable
* @param a_changeDifference change difference in coins for the coins problem
* @return bonus for given change difference
*
* @author Klaus Meffert
* @since 2.3
*/
protected double changeDifferenceBonus(double a_maxFitness,
int a_changeDifference) {
if (a_changeDifference == 0) {
return a_maxFitness;
}
else {
// we arbitrarily work with half of the maximum fitness as basis for non-
// optimal solutions (concerning change difference)
if (a_changeDifference * a_changeDifference >= a_maxFitness / 2) {
return 0.0d;
}
else {
return a_maxFitness / 2 - a_changeDifference * a_changeDifference;
}
}
}
/**
* Calculates the penalty to apply to the fitness value based on the ammount
* of coins in the solution
*
* @param a_maxFitness maximum fitness value allowed
* @param a_coins number of coins in the solution
* @return penalty for the fitness value base on the number of coins
*
* @author John Serri
* @since 2.2
*/
protected double computeCoinNumberPenalty(double a_maxFitness, int a_coins) {
if (a_coins == 1) {
// we know the solution cannot have less than one coin
return 0;
}
else {
// The more coins the more penalty, but not more than the maximum fitness
// value possible. Let's avoid linear behavior and use
// exponential penalty calculation instead
return (Math.min(a_maxFitness, a_coins * a_coins));
}
}
/**
* Calculates the total amount of change (in cents) represented by
* the given potential solution and returns that amount.
*
* @param a_potentialSolution the potential solution to evaluate
* @return The total amount of change (in cents) represented by the
* given solution
*
* @author Neil Rotstan
* @since 1.0
*/
public static int amountOfChange(IChromosome a_potentialSolution) {
int numQuarters = getNumberOfCoinsAtGene(a_potentialSolution, 0);
int numDimes = getNumberOfCoinsAtGene(a_potentialSolution, 1);
int numNickels = getNumberOfCoinsAtGene(a_potentialSolution, 2);
int numPennies = getNumberOfCoinsAtGene(a_potentialSolution, 3);
return (numQuarters * 25) + (numDimes * 10) + (numNickels * 5) +
numPennies;
}
/**
* Retrieves the number of coins represented by the given potential
* solution at the given gene position.
*
* @param a_potentialSolution the potential solution to evaluate
* @param a_position the gene position to evaluate
* @return the number of coins represented by the potential solution at the
* given gene position
*
* @author Neil Rotstan
* @since 1.0
*/
public static int getNumberOfCoinsAtGene(IChromosome a_potentialSolution,
int a_position) {
Integer numCoins =
(Integer) a_potentialSolution.getGene(a_position).getAllele();
return numCoins.intValue();
}
/**
* Returns the total number of coins represented by all of the genes in
* the given potential solution.
*
* @param a_potentialsolution the potential solution to evaluate
* @return total number of coins represented by the given Chromosome
*
* @author Neil Rotstan
* @since 1.0
*/
public static int getTotalNumberOfCoins(IChromosome a_potentialsolution) {
int totalCoins = 0;
int numberOfGenes = a_potentialsolution.size();
for (int i = 0; i < numberOfGenes; i++) {
totalCoins += getNumberOfCoinsAtGene(a_potentialsolution, i);
}
return totalCoins;
}
}