On the parallel speed-up of Estimation of Multivariate Normal Algorithm and Evolution Strategies
Abstract
Motivated by parallel optimization, we experiment EDA-like adaptation-rules in the case of $\lambda$ large. The rule we use, essentially based on estimation of multivariate normal algorithm, is (i) compliant with all families of distributions for which a density estimation algorithm exists (ii) simple (iii) parameter-free (iv) better than current rules in this framework of $\lambda$ large. The speed-up as a function of $\lambda$ is consistent with theoretical bounds.
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