Evolution Strategies with Additive Noise: A Convergence Rate Lower Bound
Résumé
We consider the problem of optimizing functions corrupted with additive noise. It is known that evolutionary algo-rithms can reach a simple regret O(1/ √ n) within logarith-mic factors, when n is the number of function evaluations. We show mathematically that this bound is tight, at least for a wide family of evolution strategies without large mutations.
Domaines
Optimisation et contrôle [math.OC]| Origine | Fichiers produits par l'(les) auteur(s) |
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