/**
* Operate on the given chromosome with the given mutation rate.
*
* @param a_chrom chromosome to operate
* @param a_rate mutation rate
* @param a_generator random generator to use (must not be null)
* @return mutated chromosome of null if no mutation has occured.
* @author Audrius Meskauskas
* @author Florian Hafner
* @since 3.3.2
*/
protected IChromosome operate(
final IChromosome a_chrom, final int a_rate, final RandomGenerator a_generator) {
IChromosome chromosome = null;
// ----------------------------------------
for (int j = m_startOffset; j < a_chrom.size(); j++) {
// Ensure probability of 1/currentRate for applying mutation.
// ----------------------------------------------------------
if (a_generator.nextInt(a_rate) == 0) {
if (chromosome == null) {
chromosome = (IChromosome) a_chrom.clone();
// In case monitoring is active, support it.
// -----------------------------------------
if (m_monitorActive) {
chromosome.setUniqueIDTemplate(a_chrom.getUniqueID(), 1);
}
}
Gene[] genes = chromosome.getGenes();
if (m_range == 0) {
m_range = genes.length;
}
Gene[] mutated = operate(a_generator, j, genes);
// setGenes is not required for this operator, but it may
// be needed for the derived operators.
// ------------------------------------------------------
try {
chromosome.setGenes(mutated);
} catch (InvalidConfigurationException cex) {
throw new Error("Gene type not allowed by constraint checker", cex);
}
}
}
return chromosome;
}
/**
* Returns the total number of coins represented by all of the genes in the given potential
* solution.
*
* @param a_potentialsolution the potential solution to evaluate
* @return total number of coins represented by the given Chromosome
* @author Neil Rotstan
* @since 1.0
*/
public static int getTotalNumberOfCoins(IChromosome a_potentialsolution) {
int totalCoins = 0;
int numberOfGenes = a_potentialsolution.size();
for (int i = 0; i < numberOfGenes; i++) {
totalCoins += getNumberOfCoinsAtGene(a_potentialsolution, i);
}
return totalCoins;
}
/**
* Marshall a Chromosome instance to an XML Element representation, including its contained Genes
* as sub-elements. This may be useful in scenarios where representation as an entire Document is
* undesirable, such as when the representation of this Chromosome is to be combined with other
* elements in a single Document.
*
* @param a_subject the chromosome to represent as an XML element
* @param a_xmlDocument a Document instance that will be used to create the Element instance. Note
* that the element will NOT be added to the document by this method
* @return an Element object representing the given Chromosome
* @author Neil Rotstan
* @since 1.0
* @deprecated use XMLDocumentBuilder instead
*/
public static Element representChromosomeAsElement(
final IChromosome a_subject, final Document a_xmlDocument) {
// Start by creating an element for the chromosome and its size
// attribute, which represents the number of genes in the chromosome.
// ------------------------------------------------------------------
Element chromosomeElement = a_xmlDocument.createElement(CHROMOSOME_TAG);
chromosomeElement.setAttribute(SIZE_ATTRIBUTE, Integer.toString(a_subject.size()));
// Next create the genes element with its nested gene elements,
// which will contain string representations of the alleles.
// --------------------------------------------------------------
Element genesElement = representGenesAsElement(a_subject.getGenes(), a_xmlDocument);
// Add the new genes element to the chromosome element and then
// return the chromosome element.
// -------------------------------------------------------------
chromosomeElement.appendChild(genesElement);
return chromosomeElement;
}
/**
* 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");
}
/**
* Compares the given Chromosome to this Chromosome. This chromosome is considered to be "less
* than" the given chromosome if it has a fewer number of genes or if any of its gene values
* (alleles) are less than their corresponding gene values in the other chromosome.
*
* @param other the Chromosome against which to compare this chromosome
* @return a negative number if this chromosome is "less than" the given chromosome, zero if they
* are equal to each other, and a positive number if this chromosome is "greater than" the
* given chromosome
* @author Neil Rotstan
* @author Klaus Meffert
* @since 1.0
*/
public int compareTo(Object other) {
// First, if the other Chromosome is null, then this chromosome is
// automatically the "greater" Chromosome.
// ---------------------------------------------------------------
if (other == null) {
return 1;
}
int size = size();
IChromosome otherChromosome = (IChromosome) other;
Gene[] otherGenes = otherChromosome.getGenes();
// If the other Chromosome doesn't have the same number of genes,
// then whichever has more is the "greater" Chromosome.
// --------------------------------------------------------------
if (otherChromosome.size() != size) {
return size() - otherChromosome.size();
}
// Next, compare the gene values (alleles) for differences. If
// one of the genes is not equal, then we return the result of its
// comparison.
// ---------------------------------------------------------------
for (int i = 0; i < size; i++) {
int comparison = getGene(i).compareTo(otherGenes[i]);
if (comparison != 0) {
return comparison;
}
}
// Compare current fitness value.
// ------------------------------
if (m_fitnessValue != otherChromosome.getFitnessValueDirectly()) {
FitnessEvaluator eval = getConfiguration().getFitnessEvaluator();
if (eval != null) {
if (eval.isFitter(m_fitnessValue, otherChromosome.getFitnessValueDirectly())) {
return 1;
} else {
return -1;
}
} else {
// undetermined order, but unequal!
// --------------------------------
return -1;
}
}
if (m_compareAppData) {
// Compare application data.
// -------------------------
if (getApplicationData() == null) {
if (otherChromosome.getApplicationData() != null) {
return -1;
}
} else if (otherChromosome.getApplicationData() == null) {
return 1;
} else {
if (getApplicationData() instanceof Comparable) {
try {
return ((Comparable) getApplicationData())
.compareTo(otherChromosome.getApplicationData());
} catch (ClassCastException cex) {
/** @todo improve */
return -1;
}
} else {
return getApplicationData()
.getClass()
.getName()
.compareTo(otherChromosome.getApplicationData().getClass().getName());
}
}
}
// Everything is equal. Return zero.
// ---------------------------------
return 0;
}
