public static void main(String[] args) throws Exception {
// Start with a DefaultConfiguration, which comes setup with the
// most common settings.
// -------------------------------------------------------------
Configuration conf = new DefaultConfiguration();
// Set the fitness function we want to use, which is our
// MinimizingMakeChangeFitnessFunction that we created earlier.
// We construct it with the target amount of change provided
// by the user.
// ------------------------------------------------------------
int targetAmount = 88;
FitnessFunction myFunc = new MinimizingMakeChangeFitnessFunction(targetAmount);
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 four genes, one for each of the coin types. We
// want the values 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, 3); // Quarters
sampleGenes[1] = new IntegerGene(conf, 0, 2); // Dimes
sampleGenes[2] = new IntegerGene(conf, 0, 1); // Nickels
sampleGenes[3] = new IntegerGene(conf, 0, 4); // Pennies
Chromosome 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 the number of potential solutions (which is good
// for finding the answer), but the longer it will take to evolve
// the population each round. We'll set the population size to
// 500 here.
// --------------------------------------------------------------
conf.setPopulationSize(10);
Genotype population = Genotype.randomInitialGenotype(conf);
for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
population.evolve();
IChromosome partialSolution = population.getFittestChromosome();
System.out.println("Solución en iteracion nro " + i + " : ");
printStatus(partialSolution);
}
IChromosome bestSolutionSoFar = population.getFittestChromosome();
System.out.println("Solución final: ");
printStatus(bestSolutionSoFar);
}
/**
* 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");
}
/**
* 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.");
}
/**
* 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);
}
public static void main(String[] args) throws InvalidConfigurationException {
// Reading data from xml
try {
new InputData().readFromFile(XML_TEST_FILENAME);
} catch (SAXException e) {
System.out.println(e.getMessage());
} catch (IOException e) {
System.out.println(e.getMessage());
} catch (ParserConfigurationException e) {
System.out.println(e.getMessage());
}
// Configuration conf = new DefaultConfiguration();
Configuration conf = new Configuration("myconf");
TimetableFitnessFunction fitnessFunction = new TimetableFitnessFunction();
InitialConstraintChecker timetableConstraintChecker = new InitialConstraintChecker();
// Creating genes
Gene[] testGenes = new Gene[CHROMOSOME_SIZE];
for (int i = 0; i < CHROMOSOME_SIZE; i++) {
testGenes[i] =
new GroupClassTeacherLessonTimeSG(
conf,
new Gene[] {
new GroupGene(conf, 1),
new ClassGene(conf, 1),
new TeacherGene(conf, 1),
new LessonGene(conf, 1),
new TimeGene(conf, 1)
});
}
System.out.println("==================================");
// Creating chromosome
Chromosome testChromosome;
testChromosome = new Chromosome(conf, testGenes);
testChromosome.setConstraintChecker(timetableConstraintChecker);
// Setup configuration
conf.setSampleChromosome(testChromosome);
conf.setPopulationSize(POPULATION_SIZE);
conf.setFitnessFunction(fitnessFunction); // add fitness function
BestChromosomesSelector myBestChromosomesSelector = new BestChromosomesSelector(conf);
conf.addNaturalSelector(myBestChromosomesSelector, false);
conf.setRandomGenerator(new StockRandomGenerator());
conf.setEventManager(new EventManager());
conf.setFitnessEvaluator(new DefaultFitnessEvaluator());
CrossoverOperator myCrossoverOperator = new CrossoverOperator(conf);
conf.addGeneticOperator(myCrossoverOperator);
TimetableMutationOperator myMutationOperator = new TimetableMutationOperator(conf);
conf.addGeneticOperator(myMutationOperator);
conf.setKeepPopulationSizeConstant(false);
// Creating genotype
// Population pop = new Population(conf, testChromosome);
// Genotype population = new Genotype(conf, pop);
Genotype population = Genotype.randomInitialGenotype(conf);
System.out.println("Our Chromosome: \n " + testChromosome.getConfiguration().toString());
System.out.println("------------evolution-----------------------------");
// Begin evolution
Calendar cal = Calendar.getInstance();
start_t = cal.getTimeInMillis();
for (int i = 0; i < MAX_EVOLUTIONS; i++) {
System.out.println(
"generation#: " + i + " population size:" + (Integer) population.getPopulation().size());
if (population.getFittestChromosome().getFitnessValue() >= THRESHOLD) break;
population.evolve();
}
cal = Calendar.getInstance();
finish_t = cal.getTimeInMillis();
System.out.println("--------------end of evolution--------------------");
Chromosome fittestChromosome = (Chromosome) population.getFittestChromosome();
System.out.println(
"-------------The best chromosome---fitness="
+ fittestChromosome.getFitnessValue()
+ "---");
System.out.println(" Group Class Time");
for (int i = 0; i < CHROMOSOME_SIZE; i++) {
GroupClassTeacherLessonTimeSG s =
(GroupClassTeacherLessonTimeSG) fittestChromosome.getGene(i);
System.out.println(
"Gene "
+ i
+ " contains: "
+ (Integer) s.geneAt(GROUP).getAllele()
+ " "
+ (Integer) s.geneAt(CLASS).getAllele()
+ " "
+ (Integer) s.geneAt(TEACHER).getAllele()
+ " "
+ (Integer) s.geneAt(LESSON).getAllele()
+ " "
+ (Integer) s.geneAt(TIME).getAllele());
// GroupGene gg = (GroupGene)s.geneAt(GROUP);
// System.out.println("gg's idGroup"+gg.getAllele()+" gg.getGroupSize()"+ gg.getGroupSize() );
}
System.out.println("Elapsed time:" + (double) (finish_t - start_t) / 1000 + "s");
// Display the best solution
OutputData od = new OutputData();
od.printToConsole(fittestChromosome);
// Write population to the disk
try {
od.printToFile(population, GENOTYPE_FILENAME, BEST_CHROMOSOME_FILENAME);
} catch (IOException e) {
System.out.println("IOException raised! " + e.getMessage());
}
}