/**
* 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.");
}
/**
* Evolves the population of chromosomes within a genotype. This will execute all of the genetic
* operators added to the present active configuration and then invoke the natural selector to
* choose which chromosomes will be included in the next generation population.
*
* @param a_pop the population to evolve
* @param a_conf the configuration to use for evolution
* @return evolved population
* @author Klaus Meffert
* @since 3.2
*/
public Population evolve(Population a_pop, Configuration a_conf) {
Population pop = a_pop;
int originalPopSize = a_conf.getPopulationSize();
boolean monitorActive = a_conf.getMonitor() != null;
IChromosome fittest = null;
// If first generation: Set age to one to allow genetic operations,
// see CrossoverOperator for an illustration.
// ----------------------------------------------------------------
if (a_conf.getGenerationNr() == 0) {
int size = pop.size();
for (int i = 0; i < size; i++) {
IChromosome chrom = pop.getChromosome(i);
chrom.increaseAge();
}
} else {
// Select fittest chromosome in case it should be preserved and we are
// not in the very first generation.
// -------------------------------------------------------------------
if (a_conf.isPreserveFittestIndividual()) {
/** @todo utilize jobs. In pop do also utilize jobs, especially for fitness computation */
fittest = pop.determineFittestChromosome(0, pop.size() - 1);
}
}
if (a_conf.getGenerationNr() > 0) {
// Adjust population size to configured size (if wanted).
// Theoretically, this should be done at the end of this method.
// But for optimization issues it is not. If it is the last call to
// evolve() then the resulting population possibly contains more
// chromosomes than the wanted number. But this is no bad thing as
// more alternatives mean better chances having a fit candidate.
// If it is not the last call to evolve() then the next call will
// ensure the correct population size by calling keepPopSizeConstant.
// ------------------------------------------------------------------
keepPopSizeConstant(pop, a_conf);
}
// Ensure fitness value of all chromosomes is udpated.
// ---------------------------------------------------
if (monitorActive) {
// Monitor that fitness value of chromosomes is being updated.
// -----------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_BEFORE_UPDATE_CHROMOSOMES1,
a_conf.getGenerationNr(),
new Object[] {pop});
}
updateChromosomes(pop, a_conf);
if (monitorActive) {
// Monitor that fitness value of chromosomes is being updated.
// -----------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_AFTER_UPDATE_CHROMOSOMES1,
a_conf.getGenerationNr(),
new Object[] {pop});
}
// Apply certain NaturalSelectors before GeneticOperators will be executed.
// ------------------------------------------------------------------------
pop = applyNaturalSelectors(a_conf, pop, true);
// Execute all of the Genetic Operators.
// -------------------------------------
applyGeneticOperators(a_conf, pop);
// Reset fitness value of genetically operated chromosomes.
// Normally, this should not be necessary as the Chromosome class
// initializes each newly created chromosome with
// FitnessFunction.NO_FITNESS_VALUE. But who knows which Chromosome
// implementation is used...
// ----------------------------------------------------------------
int currentPopSize = pop.size();
for (int i = originalPopSize; i < currentPopSize; i++) {
IChromosome chrom = pop.getChromosome(i);
chrom.setFitnessValueDirectly(FitnessFunction.NO_FITNESS_VALUE);
// Mark chromosome as new-born.
// ----------------------------
chrom.resetAge();
// Mark chromosome as being operated on.
// -------------------------------------
chrom.increaseOperatedOn();
}
// Increase age of all chromosomes which are not modified by genetic
// operations.
// -----------------------------------------------------------------
int size = Math.min(originalPopSize, currentPopSize);
for (int i = 0; i < size; i++) {
IChromosome chrom = pop.getChromosome(i);
chrom.increaseAge();
// Mark chromosome as not being operated on.
// -----------------------------------------
chrom.resetOperatedOn();
}
// If a bulk fitness function has been provided, call it.
// ------------------------------------------------------
BulkFitnessFunction bulkFunction = a_conf.getBulkFitnessFunction();
if (bulkFunction != null) {
if (monitorActive) {
// Monitor that bulk fitness will be called for evaluation.
// --------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_BEFORE_BULK_EVAL,
a_conf.getGenerationNr(),
new Object[] {bulkFunction, pop});
}
/** @todo utilize jobs: bulk fitness function is not so important for a prototype! */
bulkFunction.evaluate(pop);
if (monitorActive) {
// Monitor that bulk fitness has been called for evaluation.
// ---------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_AFTER_BULK_EVAL,
a_conf.getGenerationNr(),
new Object[] {bulkFunction, pop});
}
}
// Ensure fitness value of all chromosomes is udpated.
// ---------------------------------------------------
if (monitorActive) {
// Monitor that fitness value of chromosomes is being updated.
// -----------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_BEFORE_UPDATE_CHROMOSOMES2,
a_conf.getGenerationNr(),
new Object[] {pop});
}
updateChromosomes(pop, a_conf);
if (monitorActive) {
// Monitor that fitness value of chromosomes is being updated.
// -----------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_AFTER_UPDATE_CHROMOSOMES2,
a_conf.getGenerationNr(),
new Object[] {pop});
}
// Apply certain NaturalSelectors after GeneticOperators have been applied.
// ------------------------------------------------------------------------
pop = applyNaturalSelectors(a_conf, pop, false);
// Fill up population randomly if size dropped below specified percentage
// of original size.
// ----------------------------------------------------------------------
if (a_conf.getMinimumPopSizePercent() > 0) {
int sizeWanted = a_conf.getPopulationSize();
int popSize;
int minSize = (int) Math.round(sizeWanted * (double) a_conf.getMinimumPopSizePercent() / 100);
popSize = pop.size();
if (popSize < minSize) {
IChromosome newChrom;
IChromosome sampleChrom = a_conf.getSampleChromosome();
Class sampleChromClass = sampleChrom.getClass();
IInitializer chromIniter =
a_conf.getJGAPFactory().getInitializerFor(sampleChrom, sampleChromClass);
while (pop.size() < minSize) {
try {
/**
* @todo utilize jobs as initialization may be time-consuming as invalid combinations
* may have to be filtered out
*/
newChrom = (IChromosome) chromIniter.perform(sampleChrom, sampleChromClass, null);
if (monitorActive) {
// Monitor that fitness value of chromosomes is being updated.
// -----------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_BEFORE_ADD_CHROMOSOME,
a_conf.getGenerationNr(),
new Object[] {pop, newChrom});
}
pop.addChromosome(newChrom);
} catch (Exception ex) {
throw new RuntimeException(ex);
}
}
}
}
IChromosome newFittest = reAddFittest(pop, fittest);
if (monitorActive && newFittest != null) {
// Monitor that fitness value of chromosomes is being updated.
// -----------------------------------------------------------
a_conf
.getMonitor()
.event(
IEvolutionMonitor.MONITOR_EVENT_READD_FITTEST,
a_conf.getGenerationNr(),
new Object[] {pop, fittest});
}
// Increase number of generations.
// -------------------------------
a_conf.incrementGenerationNr();
// Fire an event to indicate we've performed an evolution.
// -------------------------------------------------------
m_lastPop = pop;
m_lastConf = a_conf;
a_conf
.getEventManager()
.fireGeneticEvent(new GeneticEvent(GeneticEvent.GENOTYPE_EVOLVED_EVENT, this));
return pop;
}