jenes.tutorials.problem3
Class TSPGA

java.lang.Object
  jenes.GeneticAlgorithm<IntegerChromosome>
      jenes.tutorials.problem3.TSPGA

public class TSPGA
extends GeneticAlgorithm<IntegerChromosome>

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, generation, generationLimit, generationListeners, initialPopulation, MAX_HISTORY_SIZE, MIN_HISTORY_SIZE, random, randomization, resizeStrategy, statistics

Constructor Summary

TSPGA(double[][] matrix, Population<IntegerChromosome> pop, int genlimit)

Method Summary

protected void	evaluateIndividual(Individual<IntegerChromosome> individual) Evaluates a single individual.
protected void	randomizeIndividual(Individual<IntegerChromosome> individual) Performs an individual randomization.

Methods inherited from class jenes.GeneticAlgorithm

addAlgorithmEventListener, addGenerationEventListener, addStage, applyElitism, end, evaluatePopulation, evolve, evolve, getBody, getCurrentPopulation, getElitism, getElitismStrategy, getGeneration, getGenerationLimit, getHistoryAt, getHistorySize, getInitialPopulation, getNextPopulation, getRandomization, getResizeStrategy, getStatistics, isBiggerBetter, onGeneration, onInit, onStart, onStop, randomizePopulation, removeAlgorithmEventListener, removeGenerationEventListener, setBiggerIsBetter, setElitism, setElitismStrategy, setGenerationLimit, setHistorySize, setRandomization, setRandomization, setResizeStrategy, start, stop, toString, updateStatistics

Methods inherited from class java.lang.Object

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

Constructor Detail

TSPGA

public TSPGA(double[][] matrix,
             Population<IntegerChromosome> pop,
             int genlimit)

Method Detail

evaluateIndividual

protected 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, thus it is an abstract method requiring an implementation by the sublass.

Specified by:

evaluateIndividual in class GeneticAlgorithm<IntegerChromosome>

Parameters:

individual - the individual to be evaluated

randomizeIndividual

protected void randomizeIndividual(Individual<IntegerChromosome> individual)

Description copied from class: GeneticAlgorithm

Performs an individual randomization. It is invoked by GeneticAlgorithm.randomizePopulation(Population). By default randomization is delegated to the individual. In some problems, it would be useful to control the randomization process of individual. This is especially the case of when there are some constraints on genes in order to make the individual valid.

Overrides:

randomizeIndividual in class GeneticAlgorithm<IntegerChromosome>

Parameters:

individual - the individual to be randomize