/*
* 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.supergene;
import org.jgap.*;
import org.jgap.impl.*;
import examples.supergene.*;
/**
* Sample fitness function for the MakeChange example, including supergenes.
*
* @author Neil Rotstan
* @author Klaus Meffert
* @author Audrius Meskauskas
* @since 2.0
*/
public abstract class AbstractChangeFitnessFunction
extends FitnessFunction {
/** String containing the CVS revision. Read out via reflection!*/
private final static String CVS_REVISION = "$Revision: 1.2 $";
private final int m_targetAmount;
public AbstractChangeFitnessFunction(int a_targetAmount) {
if (a_targetAmount < 1 || a_targetAmount > 99) {
throw new IllegalArgumentException(
"Change amount must be between 1 and 99 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 integer reflecting the fitness rating of the given
* Chromosome
* @since 2.0
*/
public double evaluate(IChromosome a_subject) {
// 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. If the amount equals 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);
// Step 1: Determine distance of amount represented by solution from
// the target amount. Since we know the maximum amount of change is
// 99 cents, we'll subtract the difference in change between the
// solution amount and the target amount from 99. 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.
// -----------------------------------------------------------------
int fitness = (99 - changeDifference);
// Step 2: If the solution amount equals the target amount, then
// we add additional fitness points for solutions representing fewer
// total coins.
// -----------------------------------------------------------------
if (changeAmount == m_targetAmount) {
// was fitness += 100 - (10 * totalCoins);
// The function should be more tolearant to the large amount of coins:
// -------------------------------------------------------------------
fitness += 100 - totalCoins;
}
// Make sure fitness value is always positive.
// -------------------------------------------
return Math.max(1, fitness);
}
/**
* 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
*/
public int amountOfChange(IChromosome a_potentialSolution) {
int numQuarters = getNumberOfCoinsAtGene(a_potentialSolution,
SupergeneSample.QUARTERS);
int numDimes = getNumberOfCoinsAtGene(a_potentialSolution,
SupergeneSample.DIMES);
int numNickels = getNumberOfCoinsAtGene(a_potentialSolution,
SupergeneSample.NICKELS);
int numPennies = getNumberOfCoinsAtGene(a_potentialSolution,
SupergeneSample.PENNIES);
return AbstractSupergeneTest.amountOfChange(numQuarters, numDimes,
numNickels, 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_code index of gene
* @return the number of coins represented by the potential solution
* at the given gene position
*/
public int getNumberOfCoinsAtGene(IChromosome a_potentialSolution,
int a_code) {
Gene g = getResponsibleGene(a_potentialSolution, a_code);
return ( (IntegerGene) g).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 the total number of coins represented by the given Chromosome
*/
public int getTotalNumberOfCoins(IChromosome a_potentialsolution) {
return
getNumberOfCoinsAtGene(a_potentialsolution, SupergeneSample.QUARTERS)
+ getNumberOfCoinsAtGene(a_potentialsolution, SupergeneSample.DIMES)
+ getNumberOfCoinsAtGene(a_potentialsolution, SupergeneSample.NICKELS)
+ getNumberOfCoinsAtGene(a_potentialsolution, SupergeneSample.PENNIES);
}
/**
* Get the gene, responsible for the number of coins, corresponding
* this code.
*
* @param a_chromosome Chromosome to evaluate
* @param a_code index of Gene
* @return responsible gene
*/
public abstract Gene getResponsibleGene(IChromosome a_chromosome, int a_code);
}