/** * Executes the genetic algorithm to determine the minimum number of items necessary to make up * the given target volume. The solution will then be written to the console. * * @param a_knapsackVolume the target volume for which this method is attempting to produce the * optimal list of items * @throws Exception * @author Klaus Meffert * @since 2.3 */ public static void findItemsForVolume(double a_knapsackVolume) 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. We construct it with // the target volume passed in to this method. // --------------------------------------------------------- FitnessFunction myFunc = new KnapsackFitnessFunction(a_knapsackVolume); 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 as many genes as there are different items available. We want the // values (alleles) of those genes to be integers, which represent // how many items 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 senseful values (i.e. maximum possible) for each item type. // -------------------------------------------------------------- Gene[] sampleGenes = new Gene[itemVolumes.length]; for (int i = 0; i < itemVolumes.length; i++) { sampleGenes[i] = new IntegerGene(conf, 0, (int) Math.ceil(a_knapsackVolume / itemVolumes[i])); } 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(50); // Create random initial population of Chromosomes. // Here we try to read in a previous run via XMLManager.readFile(..) // for demonstration purpose! // ----------------------------------------------------------------- Genotype population; try { Document doc = XMLManager.readFile(new File("knapsackJGAP.xml")); population = XMLManager.getGenotypeFromDocument(conf, doc); } catch (FileNotFoundException fex) { population = Genotype.randomInitialGenotype(conf); } 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. // --------------------------------------------------------------- for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) { population.evolve(); } // Save progress to file. A new run of this example will then be able to // resume where it stopped before! // --------------------------------------------------------------------- // 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("knapsackJGAP.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 contained the following: "); int count; double totalVolume = 0.0d; for (int i = 0; i < bestSolutionSoFar.size(); i++) { count = ((Integer) bestSolutionSoFar.getGene(i).getAllele()).intValue(); if (count > 0) { System.out.println("\t " + count + " x " + itemNames[i]); totalVolume += itemVolumes[i] * count; } } System.out.println("\nFor a total volume of " + totalVolume + " ccm"); System.out.println("Expected volume was " + a_knapsackVolume + " ccm"); System.out.println("Volume difference is " + Math.abs(totalVolume - a_knapsackVolume) + " ccm"); }
/** * Executes the genetic algorithm to determine the minimum number of items necessary to make up * the given target volume. The solution will then be written to the console. * * @param a_knapsackVolume the target volume for which this method is attempting to produce the * optimal list of items * @throws Exception * @author Klaus Meffert * @throws InvalidConfigurationException * @since 2.3 */ public static void findeBesteParameter() throws InvalidConfigurationException { // Start with a DefaultConfiguration, which comes setup with the // most common settings. // ------------------------------------------------------------- Configuration conf = new myConfiguration(); conf.setPreservFittestIndividual(true); // TODO: check: viel Mehraufwand?? ja! // conf.setAlwaysCaculateFitness(true); // Set the fitness function we want to use. We construct it with // the target volume passed in to this method. // --------------------------------------------------------- FitnessFunction myFunc = new EAmyTeamFitnessFunction(); conf.setFitnessFunction(myFunc); // --> myConfiguration // conf.addGeneticOperator(new MutationOperator(conf,2)); // conf.addGeneticOperator(new CrossoverOperator(conf, .2)); // 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. myTeamParameters dummyParam = new myTeamParameters(); // nur fuer min/max Gene[] sampleGenes = new Gene[myTeamParameters.ANZAHL_PARAMETER]; for (int i = 0; i < sampleGenes.length; i++) { sampleGenes[i] = new DoubleGene(conf, dummyParam.getMin(i), dummyParam.getMax(i)); } 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(POPULATION_SIZE); // Create random initial population of Chromosomes. // Here we try to read in a previous run via XMLManager.readFile(..) // for demonstration purpose! // ----------------------------------------------------------------- Genotype population; try { Document doc = XMLManager.readFile(new File(XML_FILENAME)); population = XMLManager.getGenotypeFromDocument(conf, doc); // TODO mit zufaelligen auffuellen?? System.out.println("Alte Population aus Datei gelesen!"); } catch (Exception fex) { 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. // --------------------------------------------------------------- for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) { System.out.println("\nPopulation Nr. " + i + ":"); printPopulation(population); population.evolve(); } // Save progress to file. A new run of this example will then be able to // resume where it stopped before! // --------------------------------------------------------------------- // represent Genotype as tree with elements Chromomes and Genes // ------------------------------------------------------------ try { 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(XML_FILENAME)); } catch (Exception e) { e.printStackTrace(); } // Display the best solution we found. // ----------------------------------- IChromosome bestSolutionSoFar = population.getFittestChromosome(); System.out.println( "The best solution has a fitness value of " + bestSolutionSoFar.getFitnessValueDirectly() + " mit folgenden Parametern:"); printChromosom(bestSolutionSoFar, true); }