/**
* Returns a new single-objective {@link DE/rand/1/bin} instance.
*
* @param properties the properties for customizing the new {@code DE/rand/1/bin} instance
* @param problem the problem
* @return a new {@code DE/rand/1/bin} instance
*/
private Algorithm newDifferentialEvolution(TypedProperties properties, Problem problem) {
if (!checkType(RealVariable.class, problem)) {
throw new FrameworkException("unsupported decision variable type");
}
int populationSize = (int) properties.getDouble("populationSize", 100);
double[] weights = properties.getDoubleArray("weights", new double[] {1.0});
String method = properties.getString("method", "linear");
AggregateObjectiveComparator comparator = null;
if (method.equalsIgnoreCase("linear")) {
comparator = new LinearDominanceComparator(weights);
} else if (method.equalsIgnoreCase("min-max")) {
comparator = new MinMaxDominanceComparator(weights);
} else {
throw new FrameworkException("unrecognized weighting method: " + method);
}
Initialization initialization = new RandomInitialization(problem, populationSize);
DifferentialEvolutionSelection selection = new DifferentialEvolutionSelection();
DifferentialEvolutionVariation variation =
(DifferentialEvolutionVariation)
OperatorFactory.getInstance().getVariation("de", properties, problem);
return new DifferentialEvolution(problem, comparator, initialization, selection, variation);
}
/**
* Returns a new single-objective {@link RepeatedSingleObjective} instance.
*
* @param properties the properties for customizing the new {@code RepeatedSingleObjective}
* instance
* @param problem the problem
* @return a new {@code RepeatedSingleObjective} instance
*/
private Algorithm newRSO(TypedProperties properties, Problem problem) {
String algorithmName = properties.getString("algorithm", "GA");
int instances = (int) properties.getDouble("instances", 100);
if (!properties.contains("method")) {
properties.setString("method", "min-max");
}
return new RepeatedSingleObjective(
problem, algorithmName, properties.getProperties(), instances);
}
/**
* Returns a new {@link CMAES} instance.
*
* @param properties the properties for customizing the new {@code CMAES} instance
* @param problem the problem
* @return a new {@code CMAES} instance
*/
private Algorithm newCMAES(TypedProperties properties, Problem problem) {
if (!checkType(RealVariable.class, problem)) {
throw new FrameworkException("unsupported decision variable type");
}
int lambda = (int) properties.getDouble("lambda", 100);
double cc = properties.getDouble("cc", -1.0);
double cs = properties.getDouble("cs", -1.0);
double damps = properties.getDouble("damps", -1.0);
double ccov = properties.getDouble("ccov", -1.0);
double ccovsep = properties.getDouble("ccovsep", -1.0);
double sigma = properties.getDouble("sigma", -1.0);
int diagonalIterations = (int) properties.getDouble("diagonalIterations", 0);
String indicator = properties.getString("indicator", "crowding");
double[] initialSearchPoint = properties.getDoubleArray("initialSearchPoint", null);
NondominatedPopulation archive = null;
FitnessEvaluator fitnessEvaluator = null;
if (problem.getNumberOfObjectives() == 1) {
archive = new NondominatedPopulation();
} else {
archive =
new EpsilonBoxDominanceArchive(
properties.getDoubleArray(
"epsilon", new double[] {EpsilonHelper.getEpsilon(problem)}));
}
if ("hypervolume".equals(indicator)) {
fitnessEvaluator = new HypervolumeFitnessEvaluator(problem);
} else if ("epsilon".equals(indicator)) {
fitnessEvaluator = new AdditiveEpsilonIndicatorFitnessEvaluator(problem);
}
CMAES cmaes =
new CMAES(
problem,
lambda,
fitnessEvaluator,
archive,
initialSearchPoint,
false,
cc,
cs,
damps,
ccov,
ccovsep,
sigma,
diagonalIterations);
return cmaes;
}
/**
* Returns a new {@link SMSEMOA} instance.
*
* @param properties the properties for customizing the new {@code SMSEMOA} instance
* @param problem the problem
* @return a new {@code SMSEMOA} instance
*/
private Algorithm newSMSEMOA(TypedProperties properties, Problem problem) {
int populationSize = (int) properties.getDouble("populationSize", 100);
double offset = properties.getDouble("offset", 100.0);
String indicator = properties.getString("indicator", "hypervolume");
FitnessEvaluator fitnessEvaluator = null;
Initialization initialization = new RandomInitialization(problem, populationSize);
Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);
if ("hypervolume".equals(indicator)) {
fitnessEvaluator = new HypervolumeContributionFitnessEvaluator(problem, offset);
}
return new SMSEMOA(problem, initialization, variation, fitnessEvaluator);
}
/**
* Returns a new single-objective {@link GeneticAlgorithm} instance.
*
* @param properties the properties for customizing the new {@code GeneticAlgorithm} instance
* @param problem the problem
* @return a new {@code GeneticAlgorithm} instance
*/
private Algorithm newGeneticAlgorithm(TypedProperties properties, Problem problem) {
int populationSize = (int) properties.getDouble("populationSize", 100);
double[] weights = properties.getDoubleArray("weights", new double[] {1.0});
String method = properties.getString("method", "linear");
AggregateObjectiveComparator comparator = null;
if (method.equalsIgnoreCase("linear")) {
comparator = new LinearDominanceComparator(weights);
} else if (method.equalsIgnoreCase("min-max")) {
comparator = new MinMaxDominanceComparator(weights);
} else {
throw new FrameworkException("unrecognized weighting method: " + method);
}
Initialization initialization = new RandomInitialization(problem, populationSize);
Selection selection = new TournamentSelection(2, comparator);
Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);
return new GeneticAlgorithm(problem, comparator, initialization, selection, variation);
}
/**
* Returns a new {@link IBEA} instance.
*
* @param properties the properties for customizing the new {@code IBEA} instance
* @param problem the problem
* @return a new {@code IBEA} instance
*/
private Algorithm newIBEA(TypedProperties properties, Problem problem) {
if (problem.getNumberOfConstraints() > 0) {
throw new ProviderNotFoundException(
"IBEA", new ProviderLookupException("constraints not supported"));
}
int populationSize = (int) properties.getDouble("populationSize", 100);
String indicator = properties.getString("indicator", "hypervolume");
IndicatorFitnessEvaluator fitnessEvaluator = null;
Initialization initialization = new RandomInitialization(problem, populationSize);
Variation variation = OperatorFactory.getInstance().getVariation(null, properties, problem);
if ("hypervolume".equals(indicator)) {
fitnessEvaluator = new HypervolumeFitnessEvaluator(problem);
} else if ("epsilon".equals(indicator)) {
fitnessEvaluator = new AdditiveEpsilonIndicatorFitnessEvaluator(problem);
} else {
throw new IllegalArgumentException("invalid indicator: " + indicator);
}
return new IBEA(problem, null, initialization, variation, fitnessEvaluator);
}
