/* * 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); } }