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POEvolution strategy with individual stepsizes
 

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  1. POEvolution strategy with individual stepsizes
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    <p>I'm trying to find a good solution with an evolution strategy for a 30 dimensional minimization problem. Now I have developed with success a simple (1,1) ES and also a self-adaptive (1,lambda) ES with one step size.</p> <p>The next step is to create a (1,lambda) ES with individual stepsizes per dimension. The problem is that my MATLAB code doesn't work yet. I'm testing on the sphere objective function:</p> <pre><code>function f = sphere(x) f = sum(x.^2); end </code></pre> <p>The plotted results of the ES with one step size vs. the one with individual stepsizes:</p> <p><a href="https://i.stack.imgur.com/hLRqI.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/hLRqI.png" alt="results"></a></p> <p>The blue line is the performance of the ES with individual step sizes and the red one is for the ES with one step size.</p> <p>The code for the (1,lambda) ES with multiple stepsizes:</p> <pre><code>% Strategy parameters tau = 1 / sqrt(2 * sqrt(N)); tau_prime = 1 / sqrt(2 * N); lambda = 10; % Initialize xp = (ub - lb) .* rand(N, 1) + lb; sigmap = (ub - lb) / (3 * sqrt(N)); fp = feval(fitnessfct, xp'); evalcount = 1; % Evolution cycle while evalcount &lt;= stopeval % Generate offsprings and evaluate for i = 1 : lambda rand_scalar = randn(); for j = 1 : N Osigma(j,i) = sigmap(j) .* exp(tau_prime * rand_scalar + tau * randn()); end O(:,i) = xp + Osigma(:,i) .* rand(N,1); fo(i) = feval(fitnessfct, O(:,i)'); end evalcount = evalcount + lambda; % Select best [~, sortindex] = sort(fo); xp = O(:,sortindex(1)); fp = fo(sortindex(1)); sigmap = Osigma(:,sortindex(1)); end </code></pre> <p>Does anybody see the problem? </p>
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