org.evosuite.ga.metaheuristics

Class SPEA2<T extends Chromosome>

java.lang.Object

org.evosuite.ga.metaheuristics.GeneticAlgorithm<T>

org.evosuite.ga.metaheuristics.SPEA2<T>

All Implemented Interfaces:

Serializable, SearchAlgorithm

public class SPEA2<T extends Chromosome>
extends GeneticAlgorithm<T>

SPEA2 implementation.

Author:

José Campos

See Also:

Serialized Form

Field Summary

Fields inherited from class org.evosuite.ga.metaheuristics.GeneticAlgorithm

bloatControl, chromosomeFactory, crossoverFunction, currentIteration, fitnessFunctions, listeners, localObjective, localSearchProbability, population, populationLimit, selectionFunction, stoppingConditions

Constructor Summary

Constructors

Constructor and Description
SPEA2(ChromosomeFactory<T> factory)

Method Summary

Modifier and Type	Method and Description
protected void	computeStrength(List<T> solution)
protected double	distanceBetweenObjectives(T t1, T t2) Returns the euclidean distance between a pair of solutions in the objective space.
protected List<T>	environmentalSelection(List<T> union)
protected double[][]	euclideanDistanceMatrix(List<T> solution) Returns a matrix with the euclidean distance between each pair of solutions in the population.
protected void	evolve() Generate one new generation
void	generateSolution() Generate solution
void	initializePopulation() Set up initial population

Methods inherited from class org.evosuite.ga.metaheuristics.GeneticAlgorithm

addBloatControl, addFitnessFunction, addFitnessFunctions, addListener, addStoppingCondition, applyLocalSearch, calculateFitnessAndSortPopulation, clearPopulation, disableFirstSecondaryCriterion, elitism, enableFirstSecondaryCriterion, generateInitialPopulation, generateRandomPopulation, getAge, getBestIndividual, getBestIndividuals, getBudgetString, getFitnessFunction, getFitnessFunctions, getNumberOfFitnessFunctions, getPopulation, getPopulationSize, getSelectionFunction, getStoppingConditions, isBetterOrEqual, isFinished, isNextPopulationFull, isTooLong, notifyEvaluation, notifyIteration, notifyMutation, notifySearchFinished, notifySearchStarted, printBudget, randomism, removeListener, removeStoppingCondition, resetStoppingConditions, setArchive, setBloatControl, setChromosomeFactory, setCrossOverFunction, setPopulationLimit, setSelectionFunction, setStoppingCondition, setStoppingConditionLimit, shouldApplyLocalSearch, sortPopulation, starveByFitness, starveRandomly, starveToLimit, toString, updateBestIndividualFromArchive, updateFitnessFunctionsAndValues, updateProbability, updateSecondaryCriterion, writeIndividuals

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

SPEA2

public SPEA2(ChromosomeFactory<T> factory)

Method Detail

evolve

protected void evolve()

Description copied from class: GeneticAlgorithm

Generate one new generation

Specified by:

evolve in class GeneticAlgorithm<T extends Chromosome>

initializePopulation

public void initializePopulation()

Description copied from class: GeneticAlgorithm

Set up initial population

Specified by:

initializePopulation in class GeneticAlgorithm<T extends Chromosome>

generateSolution

public void generateSolution()

Description copied from class: GeneticAlgorithm

Generate solution Generate solution

Specified by:

generateSolution in interface SearchAlgorithm

Specified by:

generateSolution in class GeneticAlgorithm<T extends Chromosome>

environmentalSelection

protected List<T> environmentalSelection(List<T> union)

Parameters:

population -

Returns:

computeStrength

protected void computeStrength(List<T> solution)

Parameters:

solutions -

euclideanDistanceMatrix

protected double[][] euclideanDistanceMatrix(List<T> solution)

Returns a matrix with the euclidean distance between each pair of solutions in the population.

Parameters:

solution -

Returns:

distanceBetweenObjectives

protected double distanceBetweenObjectives(T t1,
                                           T t2)

Returns the euclidean distance between a pair of solutions in the objective space.

Parameters:

t1 -

t2 -

Returns: