Hello,
I am trying to optimize a function and its standard deviation, when the input variables are changing a little bit (Monte Carlo).
function out = fitnessfcn(Y)disp('MonteCarlo')X_MCS = MonteCarlo(Y); t = @(x) x(1)^X(2)-sin(x(2)); %just an example function
temp = zeros(length(X_MCS),1);for i=1:length(X_MCS) temp(i,1) = t(X_MCS(i,:));endout = [std(temp), t(Y)];end
[solution,Fval] = gamultiobj(@fitnessfcn,n,A,b,[],[],lb,ub,@(X)nonlin(X),optsmulti);
So I thought now in every generation the whole population runs through my Monte Carlo simulation. PopulationSize is 50, so I should see 50 times my disp 'Monte Carlo' in the command window before a new generations starts, but it looks like this:
(5x) MonteCarloGeneration Func-count Pareto distance Pareto spread 1 51 1 1(2x) MonteCarlo 2 101 0 1(13x) MonteCarlo 3 151 0 1
How can I achieve that the whole population runs through my fitnessfcn, not just some random parts?
Best Answer