keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms

Class AlgorithmGAPSteady

java.lang.Object

keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.GeneticAlgorithm

keel.Algorithms.Fuzzy_Rule_Learning.Genetic.Shared.Algorithms.AlgorithmGAPSteady

public class AlgorithmGAPSteady
extends GeneticAlgorithm

AlgorithmGAPSteady is the genetic algorithm and programming (GAP) algorithm when the Steady option is chosen, that is, the Steady parameter of the given method is marked but the Niches is not. This class is an specification of GeneticAlgorithm.

Since:

JDK1.4

Version:

1.0

Author:

Written by Luciano Sanchez (University of Oviedo) 20/01/2004, Modified by J.R. Villar (University of Oviedo) 19/12/2008

Constructor Summary

Constructors

Constructor and Description
AlgorithmGAPSteady(GeneticIndividual initialIndividual, int pPopSize, int pTourSize, double PM, double AMP, double PMG, double pGACrossoverProb, double pGAMutationProb, int pGenerations, double pLOptProb, int NOL, int IOL, Randomize r, int pGACrossoverID, int pGPCrossoverID, int pGAMutationID, int pGPMutationID) Class constructor with the following parameters:

Method Summary

Modifier and Type	Method and Description
GeneticIndividual	evolve(int MAXITER) this method is intended for evolving the algorithm for a given number of iterations with an steady with niches GAP algorithm.

Methods inherited from class java.lang.Object

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

Constructor Detail

AlgorithmGAPSteady

public AlgorithmGAPSteady(GeneticIndividual initialIndividual,
                          int pPopSize,
                          int pTourSize,
                          double PM,
                          double AMP,
                          double PMG,
                          double pGACrossoverProb,
                          double pGAMutationProb,
                          int pGenerations,
                          double pLOptProb,
                          int NOL,
                          int IOL,
                          Randomize r,
                          int pGACrossoverID,
                          int pGPCrossoverID,
                          int pGAMutationID,
                          int pGPMutationID)

Class constructor with the following parameters:

Parameters:

initialIndividual - a GeneticIndividual to start the search process with the desired type of individual

pPopSize - an int with the population size

pTourSize - an int with the number of individuals that must be chosen for the tournament

PM - a double with the mutation probability

AMP - a double with the mutation amplitude

PMG - a double with the migration probability

pGACrossoverProb - a double with the genetic algorithm crossover operation probability

pGAMutationProb - a double with the genetic algorithm mutation operation probability

pGenerations - an int with the number of iterations to be carried out

pLOptProb - a double with the local optimization method probability

NOL - an int with the number of iterations in the local optimization method

IOL - an int with the local identification method identidication

r - the Randomize object

pGACrossoverID - the genetic algorithm crossover operation used attending the the current Genotype

pGPCrossoverID - the genetic algorithm crossover operation used attending the the current Genotype

pGAMutationID - the genetic algorithm crossover operation used attending the the current Genotype

pGPMutationID - the genetic algorithm crossover operation used attending the the current Genotype

Method Detail

evolve

public GeneticIndividual evolve(int MAXITER)
                         throws invalidCrossover,
                                invalidMutation,
                                invalidOptim

this method is intended for evolving the algorithm for a given number of iterations with an steady with niches GAP algorithm. The basic steps for each iteration are: the tournament selection and the genetic operations are carried out, the offsprings are evaluated and sorted with the current population.

Specified by:

evolve in class GeneticAlgorithm

Parameters:

MAXITER - an integer with the number of iterations torun in the evolucion

Returns:

the best GeneticIndividual found

Throws:

invalidCrossover - in case of unsupported crossover.

invalidMutation - in case of unsupported mutation.

invalidOptim - in case of local optimization operations.