Home > Mateda2.0 > ScriptsMateda > OptimizationScripts > MixtureGaussianEDAs_trajectory.m

# MixtureGaussianEDAs_trajectory

## PURPOSE

EXAMPLE 15: Continuous EDAs that learn mixtures of distributions

## SYNOPSIS

This is a script file.

## DESCRIPTION

     EXAMPLE 15:  Continuous EDAs that learn mixtures of distributions
                  for  the trajectory problem (see previous examples for
                  details on this problem)

## CROSS-REFERENCE INFORMATION

This function calls:

- RunEDA [AllStat,Cache]=RunEDA(PopSize,n,F,Card,cache,edaparams)

This function is called by:

## SOURCE CODE

    0001 % EXAMPLE 15:  Continuous EDAs that learn mixtures of distributions
    0002  %              for  the trajectory problem (see previous examples for
    0003  %              details on this problem)
    0004
    0005  global MGADSMproblem
    0006  cd ../trajectory % The spacecraft-trajectory problem instance is in this directory
    0007  load EdEdJ
    0008  cd ../Mateda2.0
    0009
    0010  NumbVar = 12;
    0011  PopSize = 5000;
    0012  F = 'EvalSaga';
    0013  Card(1,:) = [7000,0,0,0,50,300,0.01,0.01,1.05,8,-1*pi,-1*pi];
    0014  Card(2,:) = [9100,7,1,1,2000,2000,0.90,0.90,7.00,500,pi,pi];
    0015  cache  = [0,0,1,0,1];
    0016
    0017  learning_params(1:5) = {'vars','ClusterPointsKmeans',10,'sqEuclidean',1};
    0018  edaparams{1} = {'learning_method','LearnMixtureofFullGaussianModels',learning_params};
    0019  edaparams{2} = {'sampling_method','SampleMixtureofFullGaussianModels',{PopSize,3}};
    0020  edaparams{3} = {'replacement_method','best_elitism',{'fitness_ordering'}};
    0021  selparams(1:2) = {0.1,'fitness_ordering'};
    0022  edaparams{4} = {'selection_method','truncation_selection',selparams};
    0023  edaparams{5} = {'repairing_method','SetWithinBounds_repairing',{}};
    0024  edaparams{6} = {'stop_cond_method','max_gen',{5000}};
    0025  [AllStat,Cache]=RunEDA(PopSize,NumbVar,F,Card,cache,edaparams)