public static void printChromosom(IChromosome ch, boolean best) {
myTeamParameters param = EAmyTeamFitnessFunction.getParameters(ch);
for (Double val : param.getParameters()) {
System.out.print(myTeamParameters.doubleToString(val, 1) + "\t");
}
System.out.print(
"%-Gew: " + myTeamParameters.doubleToString(ch.getFitnessValueDirectly(), 2) + "\t");
if (best) System.out.println("<- curr fittest");
else System.out.println();
}
/**
* 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);
}