/**
* Test the method, returns the sum of all differences between the required and obtained excange
* amount. One exception counts as 1000 on the error score.
*/
public int test() {
int s = 0;
int e;
for (int amount = 20; amount < 100; amount++) {
try {
if (REPORT_ENABLED) {
System.out.println("EXCHANGING " + amount + " ");
}
// Do not solve cases without solutions
if (EXISTING_SOLUTIONS_ONLY) {
if (!Force.solve(amount)) {
continue;
}
}
// Need to reset the configuration because it needs to be changed each
// time when looping.
// -------------------------------------------------------------------
DefaultConfiguration.reset();
e = makeChangeForAmount(amount);
if (REPORT_ENABLED) {
System.out.println(" err " + e);
System.out.println("---------------");
}
s = s + e;
} catch (Exception ex) {
ex.printStackTrace();
s += 1000;
}
}
if (REPORT_ENABLED) {
System.out.println("Sum of errors " + s);
}
return s;
}
public void run() {
Configuration conf = new Configuration();
try {
conf.setEventManager(new EventManager());
Thread.sleep(100);
} catch (Exception ex) {
ex.printStackTrace();
}
}
public void run() {
Configuration conf = new Configuration();
try {
conf.setBulkFitnessFunction(new TestBulkFitnessFunction());
Thread.sleep(100);
} catch (Exception ex) {
ex.printStackTrace();
}
}
/**
* Main method. A single command-line argument is expected, which is the volume to create (in
* other words, 75 would be equal to 75 ccm).
*
* @param args first and single element in the array = volume of the knapsack to fill as a double
* value
* @author Klaus Meffert
* @since 2.3
*/
public static void main(String[] args) {
if (args.length != 1) {
System.out.println("Syntax: " + KnapsackMain.class.getName() + " <volume>");
} else {
try {
double volume = Double.parseDouble(args[0]);
if (volume < 1 || volume >= KnapsackFitnessFunction.MAX_BOUND) {
System.out.println(
"The <volume> argument must be between 1 and "
+ (KnapsackFitnessFunction.MAX_BOUND - 1)
+ " and can be a decimal.");
} else {
try {
findItemsForVolume(volume);
} catch (Exception e) {
e.printStackTrace();
}
}
} catch (NumberFormatException e) {
System.out.println("The <volume> argument must be a valid double value");
}
}
}
/**
* 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);
}