ga_main.java

这是多目标进化算法包

/**
 * GA_main.java
 *
 * @author Antonio J. Nebro
 * @version 1.0
 */

package jmetal.metaheuristics.singleObjective.geneticAlgorithm;

import jmetal.base.*;
import jmetal.base.operator.crossover.*   ;
import jmetal.base.operator.mutation.*    ; 
import jmetal.base.operator.selection.*   ;
import jmetal.problems.singleObjective.*  ; 
import jmetal.util.JMException;

/**
 * This class runs a single-objective genetic algorithm (GA). The GA can be 
 * a steady-state GA (class SSGA) or a generational GA (class GGA). The OneMax
 * problem is used to test the algorithms.
 */
public class GA_main {

  public static void main(String [] args) throws JMException {
    Problem   problem   ;         // The problem to solve
    Algorithm algorithm ;         // The algorithm to use
    Operator  crossover ;         // Crossover operator
    Operator  mutation  ;         // Mutation operator
    Operator  selection ;         // Selection operator
            
    int bits ; // Length of bit string in the OneMax problem
    
    bits = 512 ;
    //problem = new OneMax(bits);
    //problem = new Sphere(20, "Real") ;
    problem = new Griewank(20, "Real") ;
    
    algorithm = new SSGA(problem);
    //algorithm = new GGA(problem) ;
    
    /* Algorithm parameters*/
    algorithm.setInputParameter("populationSize",100);
    algorithm.setInputParameter("maxEvaluations",1000000);
    
    // Mutation and Crossover for Real codification 
    crossover = CrossoverFactory.getCrossoverOperator("SBXCrossover");                   
    crossover.setParameter("probability",0.9);                   
    crossover.setParameter("distributionIndex",20.0);

    mutation = MutationFactory.getMutationOperator("PolynomialMutation");                    
    mutation.setParameter("probability",1.0/problem.getNumberOfVariables());
    mutation.setParameter("distributionIndex",20.0);    
    
    /**
    // Mutation and Crossover for Binary codification 
    crossover = CrossoverFactory.getCrossoverOperator("SinglePointCrossover");                   
    crossover.setParameter("probability",0.95);                   
    mutation = MutationFactory.getMutationOperator("BitFlipMutation");                    
    mutation.setParameter("probability",1.0/bits); 
    */
    
    /* Selection Operator */
    selection = SelectionFactory.getSelectionOperator("BinaryTournament") ;                            
    
    /* Add the operators to the algorithm*/
    algorithm.addOperator("crossover",crossover);
    algorithm.addOperator("mutation",mutation);
    algorithm.addOperator("selection",selection);
 
    /* Execute the Algorithm */
    long initTime = System.currentTimeMillis();
    SolutionSet population = algorithm.execute();
    long estimatedTime = System.currentTimeMillis() - initTime;
    System.out.println("Total execution time: " + estimatedTime);

    /* Log messages */
    System.out.println("Objectives values have been writen to file FUN");
    population.printObjectivesToFile("FUN");
    System.out.println("Variables values have been writen to file VAR");
    population.printVariablesToFile("VAR");          
  }//main

} // GA_main