/**
* 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);
}
