/*
* 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.audit;
import java.io.*;
import org.jgap.*;
import org.jgap.impl.*;
import examples.*;
import org.jgap.audit.*;
import java.util.*;
/**
* Demonstrates how to use evolution monitors to stop evolution when certain
* criteria are met. You may want to rerun the example several times to see
* different criteria match. Concerning your system, an adaptation of the timed
* monitor (max. runtime) may be necessary.
* <p/>
* Please see class examples.MinimizingMakeChange for the original example,
* which was just extended by monitors here.
*
* @author Klaus Meffert
* @since 3.4.4
*/
public class EvolutionMonitorExample {
/** String containing the CVS revision. Read out via reflection!*/
private final static String CVS_REVISION = "$Revision: 1.1 $";
/**
* 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
* @throws Exception
*
* @author Klaus Meffert
* @since 3.4.4
*/
public static void makeChangeForAmount(int a_targetChangeAmount)
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);
// 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.
// ------------------------------------------------
Genotype population = Genotype.randomInitialGenotype(conf);
long startTime = System.currentTimeMillis();
// Configure monitors to let the evolution stop when defined criteria
// are met.
// ------------------------------------------------------------------
List monitors = new Vector();
monitors.add(new TimedMonitor(6));
monitors.add(new FitnessImprovementMonitor(1, 3, 5.0d));
IEvolutionMonitor monitor = new ChainedMonitors(monitors, 2);
// Start the evolution.
// --------------------
List<String> messages = population.evolve(monitor);
if (messages.size() > 0) {
for (String msg : messages) {
System.out.println("Message from monitor: " + msg+"\n");
}
}
// Evaluate the result.
// --------------------
long endTime = System.currentTimeMillis();
System.out.println("Total evolution time: " + (endTime - startTime)
+ " ms");
// 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 contained 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 to start the example.
*
* @param args ignored here
* @throws Exception
*
* @author Klaus Meffert
* @since 3.4.4
*/
public static void main(String[] args)
throws Exception {
makeChangeForAmount(93);
}
}