jenes.tutorials.old.problem2

Class PatternGA

java.lang.Object

jenes.GeneticAlgorithm<IntegerChromosome>

jenes.tutorials.old.problem2.PatternGA

public class PatternGA
extends GeneticAlgorithm<IntegerChromosome>

Tutorial showing how to extend GeneticAlgorithm and how to use the flexible and configurable breeding structure in Jenes. The problem consists in searching a pattern of integers with a given precision. Solutions flow through two different crossovers in parallel. Some are processed by a single point crossover, the other by a double point crossover. After solutions are mutated. This class implements the algorithm by extending GeneticAlgorithm.

Since:

1.0

Version:

1.0

Nested Class Summary

Nested classes/interfaces inherited from class jenes.GeneticAlgorithm

GeneticAlgorithm.ElitismStrategy, GeneticAlgorithm.ResizeStrategy, GeneticAlgorithm.Statistics

Field Summary

Fields inherited from class jenes.GeneticAlgorithm

algorithmListeners, body, DEFAULT_GENERATION_LIMIT, DEFAULT_HISTORY_SIZE, elitism, elitismStrategy, fullEvaluationForced, generation, generationLimit, generationListeners, initialPopulation, MAX_HISTORY_SIZE, MIN_HISTORY_SIZE, random, randomization, resizeStrategy, statistics

Constructor Summary

Constructors

Constructor and Description
PatternGA(Population<IntegerChromosome> pop, int numGen)

Method Summary

Methods

Modifier and Type	Method and Description
protected boolean	end() Provides the algorithm termination condition.
void	evaluateIndividual(Individual<IntegerChromosome> individual) Evaluates a single individual.
void	setPrecision(int precision)
void	setTarget(int[] target)

Methods inherited from class jenes.GeneticAlgorithm

addAlgorithmEventListener, addGenerationEventListener, addStage, applyElitism, evaluatePopulation, evaluatePopulation, evolve, evolve, evolve, getBody, getCurrentPopulation, getElitism, getElitismStrategy, getFitness, getGeneration, getGenerationLimit, getHistoryAt, getHistorySize, getInitialPopulation, getLastPopulation, getNextPopulation, getPool, getRandomization, getResizeStrategy, getRunner, getStatistics, isBiggerBetter, isFitnessChanged, isFullEvaluationForced, onGeneration, onInit, onStart, onStop, randomizeIndividual, randomizePopulation, removeAlgorithmEventListener, removeGenerationEventListener, setBiggerIsBetter, setElitism, setElitismStrategy, setFitness, setFullEvaluationForced, setGenerationLimit, setHistorySize, setRandomization, setRandomization, setRandomSeed, setResizeStrategy, setRunner, start, stop, toString, updateStatistics

Methods inherited from class java.lang.Object

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

Constructor Detail

PatternGA

public PatternGA(Population<IntegerChromosome> pop,
         int numGen)

Method Detail

setTarget

public void setTarget(int[] target)

setPrecision

public void setPrecision(int precision)

evaluateIndividual

public void evaluateIndividual(Individual<IntegerChromosome> individual)

Description copied from class: GeneticAlgorithm

Evaluates a single individual. This evaluation of individuals is specifically related to the problem to solve. If the genetic algorithm's body has a Fitness, this method calls Fitness.evaluate(jenes.population.Individual) method, otherwise method requiring an implementation by the sublass.

Overrides:

evaluateIndividual in class GeneticAlgorithm<IntegerChromosome>

Parameters:

individual - the individual to be evaluated

end

protected boolean end()

Description copied from class: GeneticAlgorithm

Provides the algorithm termination condition. By default it returns false as reaching the generation limit is the sole ending criterion Subclasses can override this method in order to provide a problem specific termination condition.

Overrides:

end in class GeneticAlgorithm<IntegerChromosome>

Returns:

true if the ga evolution reached the termination condition, false otherwise