Bandit-based Estimation of Distribution Algorithms for Noisy Optimization: Rigorous Runtime Analysis

International audienceWe show complexity bounds for noisy optimization, in frame- works in which noise is stronger than in previously published papers[19]. We also propose an algorithm based on bandits (variants of [16]) that reaches the bound within logarithmic factors. We emphasize the differ- ences with empirical derived published algorithms

OMD : Optimisation MultiDisciplinaire

http://www.emse.fr/~leriche/rapport_final_rntl_omd_public.pdfProgramme RNTL 2005 de l'Agence Nationale de la Recherch

On multiplicative noise models for stochastic search

Abstract. In this paper we investigate multiplicative noise models in the context of continuous optimization. We illustrate how some intrinsic properties of the noise model imply the failure of reasonable search algorithms for locating the optimum of the noiseless part of the objective function. Those findings are rigorously investigated on the (1 + 1)-ES for the minimization of the noisy sphere function. Assuming a lower bound on the support of the noise distribution, we prove that the (1 + 1)-ES diverges when the lower bound allows to sample negative fitness with positive probability and converges in the opposite case. We provide a discussion on the practical applications and non applications of those outcomes and explain the differences with previous results obtained in the limit of infinite search-space dimensionality.