speedyga.m

一种快速简单的遗传算法程序

% SpeedyGA is a vectorized implementation of a Simple Genetic Algorithm in Matlab
% Version 1.3
% Copyright (C) 2007, 2008, 2009  Keki Burjorjee
% Created and tested under Matlab 7 (R14). 

%  Licensed under the Apache License, Version 2.0 (the "License"); you may
%  not use this file except in compliance with the License. You may obtain 
%  a copy of the License at  
%
%  http://www.apache.org/licenses/LICENSE-2.0 
%
%  Unless required by applicable law or agreed to in writing, software 
%  distributed under the License is distributed on an "AS IS" BASIS, 
%  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
%  See the License for the specific language governing permissions and 
%  limitations under the License. 

%  Acknowledgement of the author (Keki Burjorjee) is requested, but not required, 
%  in any publication that presents results obtained by using this script 

%  Without Sigma Scaling, Stochastic Universal Sampling, and the generation of mask 
%  repositories, SpeedyGA faithfully implements the specification of a simple genetic 
%  algorithm given on pages 10,11 of M. Mitchell's book An Introduction to
%  Genetic Algorithms, MIT Press, 1996). Selection is fitness
%  proportionate.
 
len=640                    % The length of the genomes  
popSize=500;               % The size of the population (must be an even number)
maxGens=1000;              % The maximum number of generations allowed in a run
probCrossover=1;           % The probability of crossing over. 
probMutation=0.003;        % The mutation probability (per bit)
sigmaScalingFlag=1;        % Sigma Scaling is described on pg 168 of M. Mitchell's
                           % GA book. It often improves GA performance.
sigmaScalingCoeff=1;       % Higher values => less fitness pressure 

SUSFlag=1;                 % 1 => Use Stochastic Universal Sampling (pg 168 of 
                           %      M. Mitchell's GA book)
                           % 0 => Do not use Stochastic Universal Sampling
                           %      Stochastic Universal Sampling almost always
                           %      improves performance

crossoverType=2;           % 0 => no crossover
                           % 1 => 1pt crossover
                           % 2 => uniform crossover

visualizationFlag=1;       % 0 => don't visualize bit frequencies
                           % 1 => visualize bit frequencies

verboseFlag=1;             % 1 => display details of each generation
                           % 0 => run quietly

useMaskRepositoriesFlag=1; % 1 => draw uniform crossover and mutation masks from 
                           %      a pregenerated repository of randomly generated bits. 
                           %      Significantly improves the speed of the code with
                           %      no apparent changes in the behavior of
                           %      the SGA
                           % 0 => generate uniform crossover and mutation
                           %      masks on the fly. Slower. 
                           

    


% crossover masks to use if crossoverType==0.
mutationOnlycrossmasks=false(popSize,len);

% pre-generate two 搑epositories