private double computeFitness(IGPProgram a_program, Variable vx) {
double error = 0.0f;
Object[] noargs = new Object[0];
// Initialize local stores.
// ------------------------
a_program.getGPConfiguration().clearStack();
a_program.getGPConfiguration().clearMemory();
// Compute fitness for each program.
// ---------------------------------
for (int i = 2; i < 15; i++) {
for (int j = 0; j < a_program.size(); j++) {
vx.set(new Integer(i));
try {
try {
// Only evaluate after whole GP program was run.
// ---------------------------------------------
if (j == a_program.size() - 1) {
double result = a_program.execute_int(j, noargs);
error += Math.abs(result - fib_iter(i));
} else {
a_program.execute_void(j, noargs);
}
} catch (IllegalStateException iex) {
error = Double.MAX_VALUE / 2;
break;
}
} catch (ArithmeticException ex) {
System.out.println("Arithmetic Exception with x = " + i);
System.out.println(a_program.getChromosome(j));
throw ex;
}
}
}
return error;
}
/**
* Determine the fitness of the given Chromosome instance. The higher the return value, the more
* fit the instance. This method should always return the same fitness value for two equivalent
* Chromosome instances.
*
* @param a_subject the Chromosome instance to evaluate
* @return positive double reflecting the fitness rating of the given Chromosome
* @since 2.0 (until 1.1: return type int)
* @author Neil Rotstan, Klaus Meffert, John Serri
*/
public double evaluate(IChromosome a_subject) {
// Take care of the fitness evaluator. It could either be weighting higher
// fitness values higher (e.g.DefaultFitnessEvaluator). Or it could weight
// lower fitness values higher, because the fitness value is seen as a
// defect rate (e.g. DeltaFitnessEvaluator)
boolean defaultComparation = a_subject.getConfiguration().getFitnessEvaluator().isFitter(2, 1);
// The fitness value measures both how close the value is to the
// target amount supplied by the user and the total number of coins
// represented by the solution. We do this in two steps: first,
// we consider only the represented amount of change vs. the target
// amount of change and return higher fitness values for amounts
// closer to the target, and lower fitness values for amounts further
// away from the target. Then we go to step 2, which returns a higher
// fitness value for solutions representing fewer total coins, and
// lower fitness values for solutions representing more total coins.
// ------------------------------------------------------------------
int changeAmount = amountOfChange(a_subject);
int totalCoins = getTotalNumberOfCoins(a_subject);
int changeDifference = Math.abs(m_targetAmount - changeAmount);
double fitness;
if (defaultComparation) {
fitness = 0.0d;
} else {
fitness = MAX_BOUND / 2;
}
// Step 1: Determine distance of amount represented by solution from
// the target amount. If the change difference is greater than zero we
// will divide one by the difference in change between the
// solution amount and the target amount. That will give the desired
// effect of returning higher values for amounts closer to the target
// amount and lower values for amounts further away from the target
// amount.
// In the case where the change difference is zero it means that we have
// the correct amount and we assign a higher fitness value.
// ---------------------------------------------------------------------
if (defaultComparation) {
fitness += changeDifferenceBonus(MAX_BOUND / 2, changeDifference);
} else {
fitness -= changeDifferenceBonus(MAX_BOUND / 2, changeDifference);
}
// Step 2: We divide the fitness value by a penalty based on the number of
// coins. The higher the number of coins the higher the penalty and the
// smaller the fitness value.
// And inversely the smaller number of coins in the solution the higher
// the resulting fitness value.
// -----------------------------------------------------------------------
if (defaultComparation) {
fitness -= computeCoinNumberPenalty(MAX_BOUND / 2, totalCoins);
} else {
fitness += computeCoinNumberPenalty(MAX_BOUND / 2, totalCoins);
}
// Make sure fitness value is always positive.
// -------------------------------------------
return Math.max(1.0d, fitness);
}
/**
* Find and print the solution, return the solution error.
*
* @param a_conf the configuration to use
* @return absolute difference between the required and computed change
*/
protected int solve(
Configuration a_conf,
int a_targetChangeAmount,
SupergeneChangeFitnessFunction a_fitnessFunction,
Gene[] a_sampleGenes)
throws InvalidConfigurationException {
IChromosome sampleChromosome = new Chromosome(a_conf, a_sampleGenes);
a_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). We'll just set
// the population size to 500 here.
// ------------------------------------------------------------
a_conf.setPopulationSize(POPULATION_SIZE);
// Create random initial population of Chromosomes.
// ------------------------------------------------
Genotype population = Genotype.randomInitialGenotype(a_conf);
int s;
Evolution:
// Evolve the population, break if the the change solution is found.
// -----------------------------------------------------------------
for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
population.evolve();
s =
Math.abs(
a_fitnessFunction.amountOfChange(population.getFittestChromosome())
- a_targetChangeAmount);
if (s == 0) {
break Evolution;
}
}
// Display the best solution we found.
// -----------------------------------
IChromosome bestSolutionSoFar = report(a_fitnessFunction, population);
return Math.abs(a_fitnessFunction.amountOfChange(bestSolutionSoFar) - a_targetChangeAmount);
}
/**
* 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");
}
