% This is a reference for writing evaluation, operator, selection and% termination functions for the genetic optimization toolbox.% Binary and Real-Valued Simulation Evolution for Matlab % Copyright (C) 1996 C.R. Houck, J.A. Joines, M.G. Kay %% C.R. Houck, J.Joines, and M.Kay. A genetic algorithm for function% optimization: A Matlab implementation. ACM Transactions on Mathmatical% Software, Submitted 1996.%% This program is free software; you can redistribute it and/or modify% it under the terms of the GNU General Public License as published by% the Free Software Foundation; either version 1, or (at your option)% any later version.%% This program is distributed in the hope that it will be useful,% but WITHOUT ANY WARRANTY; without even the implied warranty of% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the% GNU General Public License for more details. A copy of the GNU % General Public License can be obtained from the % Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.echo offdone =0;while ~done  K = menu('Choose a topic','Evaluation','Operators','Selection',...    'Termination','Quit');    if(K==1)    clc;    disp('EVALUATION');    disp(' The evaluation function is the driving force behind the GA.  The');    disp(' evaluation function is called from the GA to determine the');    disp(' fitness of each solution string generated during the search.  An');    disp(' example evaluation function is given below:');    disp(' ');    disp(' function [x, val] = gaDemo1Eval(sol,options)');    disp(' x=sol(1);');    disp(' val = x + 10*sin(5*x)+7*cos(4*x);    ');    disp(' ');    disp(' Note that the evaluation function must take two parameters,');    disp(' sol and options.  Sol is a row vector of n+1 elements where');    disp(' the first n elements are the parameters of interest.  The');    disp(' n+1th element is the value of this solution.  The options');    disp(' matrix is a row matrix of');    disp(' ');    disp(' [current generation, options]');    disp(' ');    disp(' The eval function must return both the value of the string,');    disp(' val and the string itself, sol.  This is done so that');    disp(' your evaluation can repair or improve the string.');    disp(' ');    disp(' An evaluation function is unique to the optimization of the');    disp(' problem at hand, therefore, every time the ga is used for a');    disp(' different problem, an evaluation function must be developed to');    disp(' determine the fitness of the individuals.');  end  if(K==2)    clc;        disp('OPERATORS');    disp(' Operators provide the search mechanism of the GA.  The');    disp(' operators are used to create new solutions based on existing');    disp('solutions in the population.  There are two basic types of');    disp(' operators, crossover and mutation.  Crossover takes two');    disp(' individuals and produces two new individuals while mutation');    disp(' alters one individual to produce a single new solution.  The');    disp(' ga function calls each of the operators to produce new');    disp(' solutions.  The function call for crossovers is as follows:');    disp('');    disp(' function [c1,c2] =crossover(p1,p2,bounds,Ops)');     disp('');    disp('where');    disp(' p1 is the first parent ([solution_string function_value])');    disp(' p2 is the second parent ([solution_string function_value])');    disp(' bounds is the bounds matrix for the solution space');            disp(' ops is a vector of information, i.e. ');    disp('[current_generation crossover_ops]');    disp(' while the mutation function call is');    disp(' similar but only takes one parent and returns one child.');    disp(' function [c1] = mutation(p1,bounds,Ops)');    disp(' ');    disp(' The crossover operator must take all 4 arguments,');    disp(' the two parents, the bounds of the search space,');    disp(' the information on how much of the evolution has');    disp(' taken place and any other special options required,');    disp(' and similarly mutations must all take the three');    disp(' arguments and return the resulting');    disp(' child. ');  end  if(K==3)    clc;        disp('SELECTION')    disp(' The selection function determines which');    disp(' of the individuals will survive and continue');    disp(' on to the next generation.  The ga function');    disp(' calls the selection function each generation');    disp(' after all the new children have been');    disp(' evaluated to determine their fitness using');    disp(' the user provided evaluation function.');    disp(' ');    disp(' The basic function call used in the ga for');    disp(' selection is: ');    disp(' ');    disp(' function[newPop] = selectFunction(oldPop,options) ');    disp(' ');    disp(' where newPop is the new population selected, ');    disp(' oldPop is the current population, ');    disp(' options is a vector for any other optional parameters.');    disp(' ');    disp(' Notice that all selection routines must take');    disp(' three parameters, the old population from');    disp(' which to select members from, and any');    disp(' specific options to that particular selection');    disp(' routine.  The function must return the new');    disp(' population.');  end  if(K==4)    clc;    disp('TERMINATION')    disp(' The termination function determines when to');    disp(' stop the simulated evolution and return the');    disp(' resulting population.  The ga function calls');    disp(' the termination function once every');    disp(' generation after the application of all of');    disp(' the operator functions and the evaluation');    disp(' function for the resulting children.  The');    disp(' function call is of the format:');    disp(' ');    disp(' done = terminateFunction(options,bestPop,pop)');    disp(' ');    disp(' options is a vector of termination options');    disp(' the first of which is always the current generation');    disp(' bestPop is a matrix of the best individuals and the respective');    disp(' generation it was found.  ');    disp(' pop is the current population.');  end  if(K==5)    done=1;  endend