/**
* Main method. A single command-line argument is expected, which is the amount of change to
* create (in other words, 75 would be equal to 75 cents).
*
* @param args amount of change in cents to create
* @throws Exception
* @author Neil Rotstan
* @author Klaus Meffert
* @since 1.0
*/
public static void main(String[] args) throws Exception {
if (args.length < 1) {
System.out.println("Syntax: ConstraintExample <amount>");
} else {
int amount = 0;
try {
amount = Integer.parseInt(args[0]);
} catch (NumberFormatException e) {
System.out.println("The <amount> argument must be a valid integer value");
System.exit(1);
}
boolean doMonitor = false;
if (args.length > 1) {
String monitoring = args[1];
if (monitoring != null && monitoring.equals("MONITOR")) {
doMonitor = true;
}
}
makeChangeForAmount(amount, doMonitor);
}
}
/**
* Executes the genetic algorithm to determine the minimum number of coins necessary to make up
* the given target amount of change. The solution will then be written to System.out.
*
* @param a_targetChangeAmount the target amount of change for which this method is attempting to
* produce the minimum number of coins
* @param a_doMonitor true: turn on monitoring for later evaluation of evolution progress
* @throws Exception
* @author Neil Rotstan
* @author Klaus Meffert
* @since 1.0
*/
public static void makeChangeForAmount(int a_targetChangeAmount, boolean a_doMonitor)
throws Exception {
// Start with a DefaultConfiguration, which comes setup with the
// most common settings.
// -------------------------------------------------------------
Configuration conf = new DefaultConfiguration();
// Care that the fittest individual of the current population is
// always taken to the next generation.
// Consider: With that, the pop. size may exceed its original
// size by one sometimes!
// -------------------------------------------------------------
conf.setPreservFittestIndividual(true);
conf.setKeepPopulationSizeConstant(false);
// Set the fitness function we want to use, which is our
// MinimizingMakeChangeFitnessFunction. We construct it with
// the target amount of change passed in to this method.
// ---------------------------------------------------------
FitnessFunction myFunc = new SampleFitnessFunction(a_targetChangeAmount);
conf.setFitnessFunction(myFunc);
if (a_doMonitor) {
// Turn on monitoring/auditing of evolution progress.
// --------------------------------------------------
m_monitor = new EvolutionMonitor();
conf.setMonitor(m_monitor);
}
// 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 four genes, one for each of the coin types. We want the
// values (alleles) of those genes to be integers, which represent
// how many coins 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 sensible values for each coin type.
// --------------------------------------------------------------
Gene[] sampleGenes = new Gene[4];
sampleGenes[0] = new IntegerGene(conf, 0, 98); // Wasser
sampleGenes[1] = new IntegerGene(conf, 0, 98); // Zucker
sampleGenes[2] = new IntegerGene(conf, 0, 98); // Saft1
sampleGenes[3] = new IntegerGene(conf, 0, 98); // Saft2
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(80);
// Now we initialize the population randomly, anyway (as an example only)!
// If you want to load previous results from file, remove the next line!
// -----------------------------------------------------------------------
Genotype 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.
// ---------------------------------------------------------------
long startTime = System.currentTimeMillis();
for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
if (!uniqueChromosomes(population.getPopulation())) {
throw new RuntimeException("Invalid state in generation " + i);
}
if (m_monitor != null) {
population.evolve(m_monitor);
} else {
population.evolve();
}
}
long endTime = System.currentTimeMillis();
System.out.println("Total evolution time: " + (endTime - startTime) + " ms");
// Save progress to file. A new run of this example will then be able to
// resume where it stopped before! --> this is completely optional.
// ---------------------------------------------------------------------
// Display the best solution we found.
// -----------------------------------
IChromosome bestSolutionSoFar = population.getFittestChromosome();
double v1 = bestSolutionSoFar.getFitnessValue();
System.out.println(
"The best solution has a fitness value of " + bestSolutionSoFar.getFitnessValue());
bestSolutionSoFar.setFitnessValueDirectly(-1);
System.out.println("It contains the following: ");
System.out.println(
"\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 0) + " ml water.");
System.out.println(
"\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 1) + " ml sugar.");
System.out.println(
"\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 2) + " ml juice 1.");
System.out.println(
"\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 3) + " ml juice 2.");
System.out.println(
"For a total of " + SampleFitnessFunction.amountOfChange(bestSolutionSoFar) + " ml.");
}