/*
* 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;
import java.io.*;
import org.jgap.*;
import org.jgap.audit.*;
import org.jgap.data.*;
import org.jgap.impl.*;
import org.jgap.xml.*;
import org.w3c.dom.*;
/**
* This class provides an implementation of the classic "Make change" problem
* using a genetic algorithm. The goal of the problem is to provide a
* specified amount of change (from a cash purchase) in the fewest coins
* possible. This example implementation uses American currency (quarters,
* dimes, nickels, and pennies).
* <p>
* This example may be seen as somewhat significant because it demonstrates
* the use of a genetic algorithm in a less-than-optimal problem space.
* The genetic algorithm does best when there is a smooth slope of fitness
* over the problem space towards the optimum solution. This problem exhibits
* a more choppy space with more local optima. However, as can be seen from
* running this example, the genetic algorithm still will get the correct
* (or a very close) answer virtually everytime.
*
* @author Neil Rotstan
* @author Klaus Meffert
* @since 1.0
*/
public class MinimizingMakeChange {
/** String containing the CVS revision. Read out via reflection!*/
private final static String CVS_REVISION = "$Revision: 1.25 $";
/**
* The total number of times we'll let the population evolve.
*/
private static final int MAX_ALLOWED_EVOLUTIONS = 200;
public static EvolutionMonitor m_monitor;
/**
* 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
* @param a_doMonitor true: turn on monitoring for later evaluation of
* evolution progress
*
* @throws Exception
*
* @author Neil Rotstan
* @author Klaus Meffert
* @since 1.0
*/
public static void makeChangeForAmount(int a_targetChangeAmount,
boolean a_doMonitor)
throws Exception {
// Start with a DefaultConfiguration, which comes setup with the
// most common settings.
// -------------------------------------------------------------
Configuration conf = new DefaultConfiguration();
// Care that the fittest individual of the current population is
// always taken to the next generation.
// Consider: With that, the pop. size may exceed its original
// size by one sometimes!
// -------------------------------------------------------------
conf.setPreservFittestIndividual(true);
// Set the fitness function we want to use, which is our
// MinimizingMakeChangeFitnessFunction. We construct it with
// the target amount of change passed in to this method.
// ---------------------------------------------------------
FitnessFunction myFunc =
new MinimizingMakeChangeFitnessFunction(a_targetChangeAmount);
conf.setFitnessFunction(myFunc);
if (a_doMonitor) {
// Turn on monitoring/auditing of evolution progress.
// --------------------------------------------------
m_monitor = new EvolutionMonitor();
conf.setMonitor(m_monitor);
}
// 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[0] = new IntegerGene(conf, 0, 3 * 10); // Quarters
sampleGenes[1] = new IntegerGene(conf, 0, 2 * 10); // Dimes
sampleGenes[2] = new IntegerGene(conf, 0, 1 * 10); // Nickels
sampleGenes[3] = new IntegerGene(conf, 0, 4 * 10); // Pennies
IChromosome sampleChromosome = new Chromosome(conf, sampleGenes);
conf.setSampleChromosome(sampleChromosome);
// Finally, we need to tell the Configuration object how many
// Chromosomes we want in our population. The more Chromosomes,
// the larger number of potential solutions (which is good for
// finding the answer), but the longer it will take to evolve
// the population (which could be seen as bad).
// ------------------------------------------------------------
conf.setPopulationSize(80);
// Create random initial population of Chromosomes.
// Here we try to read in a previous run via XMLManager.readFile(..)
// for demonstration purpose only!
// -----------------------------------------------------------------
Genotype population;
try {
Document doc = XMLManager.readFile(new File("JGAPExample32.xml"));
population = XMLManager.getGenotypeFromDocument(conf, doc);
}
catch (UnsupportedRepresentationException uex) {
// JGAP codebase might have changed between two consecutive runs.
// --------------------------------------------------------------
population = Genotype.randomInitialGenotype(conf);
}
catch (FileNotFoundException fex) {
population = Genotype.randomInitialGenotype(conf);
}
// Now we initialize the population randomly, anyway (as an example only)!
// If you want to load previous results from file, remove the next line!
// -----------------------------------------------------------------------
population = Genotype.randomInitialGenotype(conf);
// Evolve the population. Since we don't know what the best answer
// is going to be, we just evolve the max number of times.
// ---------------------------------------------------------------
long startTime = System.currentTimeMillis();
for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
if (!uniqueChromosomes(population.getPopulation())) {
throw new RuntimeException("Invalid state in generation "+i);
}
if(m_monitor != null) {
population.evolve(m_monitor);
}
else {
population.evolve();
}
}
long endTime = System.currentTimeMillis();
System.out.println("Total evolution time: " + ( endTime - startTime)
+ " ms");
// Save progress to file. A new run of this example will then be able to
// resume where it stopped before! --> this is completely optional.
// ---------------------------------------------------------------------
// Represent Genotype as tree with elements Chromomes and Genes.
// -------------------------------------------------------------
DataTreeBuilder builder = DataTreeBuilder.getInstance();
IDataCreators doc2 = builder.representGenotypeAsDocument(population);
// create XML document from generated tree
XMLDocumentBuilder docbuilder = new XMLDocumentBuilder();
Document xmlDoc = (Document) docbuilder.buildDocument(doc2);
XMLManager.writeFile(xmlDoc, new File("JGAPExample26.xml"));
// Display the best solution we found.
// -----------------------------------
IChromosome bestSolutionSoFar = population.getFittestChromosome();
System.out.println("The best solution has a fitness value of " +
bestSolutionSoFar.getFitnessValue());
System.out.println("It contains the following: ");
System.out.println("\t" +
MinimizingMakeChangeFitnessFunction.
getNumberOfCoinsAtGene(
bestSolutionSoFar, 0) + " quarters.");
System.out.println("\t" +
MinimizingMakeChangeFitnessFunction.
getNumberOfCoinsAtGene(
bestSolutionSoFar, 1) + " dimes.");
System.out.println("\t" +
MinimizingMakeChangeFitnessFunction.
getNumberOfCoinsAtGene(
bestSolutionSoFar, 2) + " nickels.");
System.out.println("\t" +
MinimizingMakeChangeFitnessFunction.
getNumberOfCoinsAtGene(
bestSolutionSoFar, 3) + " pennies.");
System.out.println("For a total of " +
MinimizingMakeChangeFitnessFunction.amountOfChange(
bestSolutionSoFar) + " cents in " +
MinimizingMakeChangeFitnessFunction.
getTotalNumberOfCoins(
bestSolutionSoFar) + " coins.");
}
/**
* Main method. A single command-line argument is expected, which is the
* amount of change to create (in other words, 75 would be equal to 75
* cents).
*
* @param args amount of change in cents to create
* @throws Exception
*
* @author Neil Rotstan
* @author Klaus Meffert
* @since 1.0
*/
public static void main(String[] args)
throws Exception {
if (args.length < 1) {
System.out.println("Syntax: MinimizingMakeChange <amount>");
}
else {
int amount = 0;
try {
amount = Integer.parseInt(args[0]);
}
catch (NumberFormatException e) {
System.out.println(
"The <amount> argument must be a valid integer value");
System.exit(1);
}
if (amount < 1 ||
amount >= MinimizingMakeChangeFitnessFunction.MAX_BOUND) {
System.out.println("The <amount> argument must be between 1 and "
+
(MinimizingMakeChangeFitnessFunction.MAX_BOUND - 1)
+ ".");
}
else {
boolean doMonitor = false;
if (args.length > 1) {
String monitoring = args[1];
if(monitoring != null && monitoring.equals("MONITOR")) {
doMonitor = true;
}
}
makeChangeForAmount(amount, doMonitor);
}
}
}
/**
* @param a_pop the population to verify
* @return true if all chromosomes in the populationa are unique
*
* @author Klaus Meffert
* @since 3.3.1
*/
public static boolean uniqueChromosomes(Population a_pop) {
// Check that all chromosomes are unique
for(int i=0;i<a_pop.size()-1;i++) {
IChromosome c = a_pop.getChromosome(i);
for(int j=i+1;j<a_pop.size();j++) {
IChromosome c2 =a_pop.getChromosome(j);
if (c == c2) {
return false;
}
}
}
return true;
}
}