Java tutorial
/* * 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 java.awt.image.*; import org.jfree.chart.*; import org.jfree.chart.plot.*; import org.jfree.data.category.*; import org.jgap.*; import org.jgap.impl.*; import org.jgap.audit.*; /** * Same logic as in MinimizingMakeChange except that we are using the new * audit capabilities provided by JGAP 2.2 * * @author Klaus Meffert * @since 2.2 */ public class CoinsExample { /** 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 = 80; /** * 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 Neil Rotstan * @author Klaus Meffert * @since 1.0 */ 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(); 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 CoinsExampleFitnessFunction(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 Chromosome 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(50); // Added here for demonstrating purposes is a permuting configuration. // It allows for evaluating which configuration could work best for // the given problem. // ------------------------------------------------------------------- PermutingConfiguration pconf = new PermutingConfiguration(conf); pconf.addGeneticOperatorSlot(new CrossoverOperator(conf)); pconf.addGeneticOperatorSlot(new MutationOperator(conf)); pconf.addNaturalSelectorSlot(new BestChromosomesSelector(conf)); pconf.addNaturalSelectorSlot(new WeightedRouletteSelector(conf)); pconf.addRandomGeneratorSlot(new StockRandomGenerator()); RandomGeneratorForTesting rn = new RandomGeneratorForTesting(); rn.setNextDouble(0.7d); rn.setNextInt(2); pconf.addRandomGeneratorSlot(rn); pconf.addRandomGeneratorSlot(new GaussianRandomGenerator()); pconf.addFitnessFunctionSlot(new CoinsExampleFitnessFunction(a_targetChangeAmount)); Evaluator eval = new Evaluator(pconf); /**@todo class Evaluator: * input: * + PermutingConfiguration * + Number of evaluation runs pers config (to turn off randomness * as much as possible) * + output facility (data container) * + optional: event subscribers * output: * + averaged curve of fitness value thru all generations * + best fitness value accomplished * + average number of performance improvements for all generations */ int permutation = 0; while (eval.hasNext()) { // Create random initial population of Chromosomes. // ------------------------------------------------ Genotype population = Genotype.randomInitialGenotype(eval.next()); for (int run = 0; run < 10; run++) { // Evolve the population. Since we don't know what the best answer // is going to be, we just evolve the max number of times. // --------------------------------------------------------------- for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) { population.evolve(); // Add current best fitness to chart. // ---------------------------------- double fitness = population.getFittestChromosome().getFitnessValue(); if (i % 3 == 0) { String s = String.valueOf(i); // Number n = eval.getValue("Fitness " + permutation, s); // double d; // if (n != null) { // // calculate historical average // d = n.doubleValue() + fitness/(run+1); // } // else { // d = fitness; // } eval.setValue(permutation, run, fitness, "" + permutation, s); eval.storeGenotype(permutation, run, population); // eval.setValue(permutation,run,fitness, new Integer(0), s); } } } // 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" + CoinsExampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 0) + " quarters."); System.out.println( "\t" + CoinsExampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 1) + " dimes."); System.out.println( "\t" + CoinsExampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 2) + " nickels."); System.out.println( "\t" + CoinsExampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 3) + " pennies."); System.out.println( "For a total of " + CoinsExampleFitnessFunction.amountOfChange(bestSolutionSoFar) + " cents in " + CoinsExampleFitnessFunction.getTotalNumberOfCoins(bestSolutionSoFar) + " coins."); permutation++; } // Create chart: fitness values average over all permutations. // ----------------------------------------------------------- // Construct JFreeChart Dataset. // ----------------------------- KeyedValues2D myDataset = eval.calcAvgFitness(-1); //eval.getData(); DefaultCategoryDataset dataset = new DefaultCategoryDataset(); for (int ii = 0; ii < myDataset.getColumnCount(); ii++) { for (int jj = 0; jj < myDataset.getRowCount(); jj++) { dataset.setValue(myDataset.getValue(myDataset.getRowKey(jj), myDataset.getColumnKey(ii)), "Perm " + myDataset.getRowKey(jj), myDataset.getColumnKey(ii)); } } PlotOrientation or = PlotOrientation.VERTICAL; JFreeChart chart = ChartFactory.createLineChart("JGAP: Evolution progress", "Evolution cycle", "Fitness value", dataset, or, true /*legend*/, true /*tooltips*/ , false /*urls*/); BufferedImage image = chart.createBufferedImage(640, 480); FileOutputStream fo = new FileOutputStream("c:\\JGAP_chart_fitness_values.jpg"); ChartUtilities.writeBufferedImageAsJPEG(fo, 0.7f, image); // Performance metrics for each single permutation. // ------------------------------------------------ int maxPerm = permutation - 1; double avgBestFitness = 0.0d; int avgBestGen = 0; double avgAvgFitness = 0.0d; double avgAvgDiv = 0.0d; double avgAvgBestD = 0.0d; for (int i = 0; i < maxPerm; i++) { // myDataset = eval.calcAvgFitness(i); Evaluator.GenotypeDataAvg dataAvg = eval.calcPerformance(i); System.err.println("-----------------------------"); System.err.println("Perm " + i); System.err.println("Best Fitness " + dataAvg.bestFitnessValue); System.err.println(" Generation " + dataAvg.bestFitnessValueGeneration); System.err.println(" BestFit/Gen " + dataAvg.bestFitnessValue / dataAvg.bestFitnessValueGeneration); System.err.println("Avg. Fitness " + dataAvg.avgFitnessValue); System.err.println("Avg. Div. " + dataAvg.avgDiversityFitnessValue); System.err.println("Avg. BestD " + dataAvg.avgBestDeltaFitnessValue); avgBestFitness += dataAvg.bestFitnessValue; avgBestGen += dataAvg.bestFitnessValueGeneration; avgAvgFitness += dataAvg.avgFitnessValue; avgAvgDiv += dataAvg.avgDiversityFitnessValue; avgAvgBestD += dataAvg.avgBestDeltaFitnessValue; } // Performance metrics for all permutations. // ----------------------------------------- System.err.println("\nOverall Statistics for all permutations"); System.err.println("----------------------------------------"); System.err.println("Avg. Best Fitness " + avgBestFitness / maxPerm); System.err.println("Avg. Best Generation " + avgBestGen / maxPerm); System.err.println("Avg. Avg. Fitness " + avgAvgFitness / maxPerm); System.err.println("Avg. Avg. Diversity " + avgAvgDiv / maxPerm); System.err.println("Avg. Avg. BestD " + avgAvgBestD / maxPerm); // Create chart: performance metrics for all permutations. // ----------------------------------------------------------- dataset = new DefaultCategoryDataset(); for (int ii = 0; ii < myDataset.getColumnCount(); ii++) { for (int jj = 0; jj < myDataset.getRowCount(); jj++) { dataset.setValue(myDataset.getValue(myDataset.getRowKey(jj), myDataset.getColumnKey(ii)), myDataset.getRowKey(jj), myDataset.getColumnKey(ii)); } } chart = ChartFactory.createLineChart("JGAP: Evolution progress", "Evolution cycle", "Fitness value", dataset, or, true /*legend*/, true /*tooltips*/ , false /*urls*/); image = chart.createBufferedImage(640, 480); fo = new FileOutputStream("c:\\JGAP_chart_fitness_values_1.jpg"); ChartUtilities.writeBufferedImageAsJPEG(fo, 0.7f, image); } public static void main(String[] args) { if (args.length != 1) { System.out.println("Syntax: CoinsExample <amount>"); } else { try { int amount = Integer.parseInt(args[0]); if (amount < 1 || amount >= CoinsExampleFitnessFunction.MAX_BOUND) { System.out.println("The <amount> argument must be between 1 and " + (CoinsExampleFitnessFunction.MAX_BOUND - 1) + "."); } else { try { makeChangeForAmount(amount); } catch (Exception e) { e.printStackTrace(); } } } catch (NumberFormatException e) { System.out.println("The <amount> argument must be a valid integer value"); } } } }