[hal-01077625] Evolution Strategies with Additive Noise A Convergence Rate Lower Bound

We consider the problem of optimizing functions corrupted with additive noise It is known that evolutionary algo-rithms can reach a simple regret O1/ √ 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



from HAL : Dernières publications http://ift.tt/12YLAWB