Dear,
I used genetic algorithm from matlab toolbox and I got a good min result using this options.
function [x,fval,exitflag,output,population,score] = optim(nvars,PopulationSize_Data,Generations_Data,InitialPopulation_Data)
options = gaoptimset;
options = gaoptimset(options,'PopulationSize', PopulationSize_Data);
options = gaoptimset(options,'Generations', Generations_Data);
options = gaoptimset(options,'InitialPopulation', InitialPopulation_Data);
options = gaoptimset(options,'Display', 'off');
options = gaoptimset(options,'PlotFcns', { @gaplotbestf @gaplotbestindiv});
[x,fval,exitflag,output,population,score] = ...
ga(@fitness,nvars,[],[],[],[],[],[],[],[],options);
But because it cost a lot off time I wrote my code by my self using the same options. See the code
clc;clear all;close all;format short g;
for ki=1:1
clear bestSofar
clear totalFitness
tic;
m=2;
hold on
populationSize=200;
populationSize = 4*ceil(populationSize/4);
corssOverPercentage=0.8;
noOfElites=10;
noOfGeneration=200;
noOfCrossOvers=corssOverPercentage*populationSize;
noOFMutaion=10;
for i=1: populationSize
here initialize population x;
end
z=0;
for l=1:noOfGeneration
y=x;
for i=1:populationSize
FitnessValue;
end
xFit= FitnessValue';
fitCumSum=1/cumsum(xFit) ;
rouletteWheel=fitCumSum/fitCumSum(end);
y=[y,xFit];
[j,k]=sort(y(:,end),'descend');
y=y(k,:);
y=y(:,1:end-1);
size(y);
for crindex=1:2:noOfCrossOvers-1
ran=rand;
for i=1:populationSize-1
if (ran>rouletteWheel(i))
parent1=x(i+1,:);
parent1Index=i+1;
end
end
ran=rand;
for i=1:populationSize-1
if (ran>rouletteWheel(i))
parent2=x(i+1,:);
parent2Index=i+1;
end
end
end
end
x=y;
totalFitness(l)=sum(xFit);
sortedFit=sort(xFit,'descend');
bestSofar(l)=sortedFit(end);
if l>1 & bestSofar(l)< bestSofar(l-1)
z=0;
else
z=z+1;
end
if z==500 break
end
end
finalSolution=x(end,:)
end
figure;
z=finalSolution;
Plot(z);
from this code I got a result but is not as good as the code from toolbox. I checked the selection operator it has no effect on the result. this code works perfect for 2 dimensions with number generation=100, but for m>=3 I got this different in result, I need to minimize a shape but is not as minimum as the result from matlab toolbox. Can any one tell me what is missed in it. I tried to read some code from the GA toolbox to see what is missed but I couldn't.
I checked and I found when I set the generation less than 100 I got not bad result, the problem I need the GA stop when the result reach the minimum value by itself without specifying Number generation by my self. I think the stop criteria in this code is not reliable like the GA from toolbox.
Nadia
Thanks for any help
