/*
* 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.*;
/**
* Computes the optimal change with the same condition as
* SupergeneTest, but without using supergenes. Implemented
* to compare the performance.
* To test the Supergene, we created the "makechange" version with
* additional condition: the number of nickels and pennies must be
* both even or both odd. The supergene encloses two genes
* (nickels and pennies) and is valid if the condition above is
* satisfied.
*
* @author Audrius Meskauskas
* @author Klaus Meffert
*/
class WithoutSupergeneSample
extends SupergeneSample {
/** String containing the CVS revision. Read out via reflection!*/
private final static String CVS_REVISION = "0.0.0 alpha explosive";
/**
* Executes the genetic algorithm to determine the minimum number of
* coins necessary to make up the given target amount of change. The
* solution will then be written to System.out.
*
* @param a_targetChangeAmount The target amount of change for which this
* method is attempting to produce the minimum number of coins
* @return absolute difference between the required and computed change
* @throws Exception
*/
public int makeChangeForAmount(int a_targetChangeAmount)
throws Exception {
// Start with a DefaultConfiguration, which comes setup with the
// most common settings.
// -------------------------------------------------------------
Configuration conf = new DefaultConfiguration();
// Set the fitness function we want to use. We construct it with
// the target amount of change passed in to this method.
// ---------------------------------------------------------
WithoutSupergeneChangeFitFForTesting fitnessFunction =
new WithoutSupergeneChangeFitFForTesting(a_targetChangeAmount);
conf.setFitnessFunction(fitnessFunction);
// Now we need to tell the Configuration object how we want our
// Chromosomes to be setup. We do that by actually creating a
// sample Chromosome and then setting it on the Configuration
// object. As mentioned earlier, we want our Chromosomes to each
// have four genes, one for each of the coin types. We want the
// values (alleles) of those genes to be integers, which represent
// how many coins of that type we have. We therefore use the
// IntegerGene class to represent each of the genes. That class
// also lets us specify a lower and upper bound, which we set
// to sensible values for each coin type.
// --------------------------------------------------------------
Gene[] sampleGenes = new Gene[4];
sampleGenes[DIMES] = getDimesGene(conf); // Dimes
sampleGenes[NICKELS] = getNickelsGene(conf); // Nickels
sampleGenes[QUARTERS] = getQuartersGene(conf); // Quarters
sampleGenes[PENNIES] = getPenniesGene(conf); // Pennies
int s = solve(conf, a_targetChangeAmount, fitnessFunction, sampleGenes);
return s;
}
public static void main(String[] args) {
WithoutSupergeneSample test = new WithoutSupergeneSample();
test.test();
System.exit(0);
}
}