www.gusucode.com > 遗传算法 gaot工具箱matlab源码程序 > code_rar/gaot/initializeoga.m
function [pop] = initializeoga(num,bounds,evalFN,evalOps,options) % initializeoga(populationSize, variableBounds,evalFN,evalOps,options) % initializeoga creates a matrix of random permutations with % a number of rows equal to the populationSize and a number % columns equal to the size of the permutation plus 1 for % the f(x) value which is found by applying the evalFN. % This initization function is used with an order-based % representation. % % pop - the initial, evaluated, random population % num - the size of the population, i.e. the number to create % bounds - the number of permutations in an individual (e.g., number % of cities in a tsp % evalFN - the evaluation fn, usually the name of the .m file for evaluation % evalOps- any options to be passed to the eval function defaults [] % options- options to the initialize function, ie. [eps float/binary prec] % where eps is the epsilon value and the second option is 1 for % orderOps, prec is the precision of the variables defaults [1e-6 1] if nargin<5 options=[1e-6 1]; end if nargin<4 evalOps=[]; end if any(evalFN<48) %Not a .m file estr=['x=pop(i,:); pop(i,xZomeLength)=', evalFN ';']; else %A .m file estr=['[pop(i,:) pop(i,xZomeLength)]=' evalFN '(pop(i,:),[0 evalOps]);']; end numVars = bounds; %Number of variables xZomeLength = numVars+1; %Length of string is numVar + fit pop = zeros(num,xZomeLength); %Allocate the new population for i=1:num pop(i,:)=[randperm(numVars) 0]; eval(estr); end