Bandit-based Estimation of Distribution
Algorithms for Noisy Optimization: Rigorous
We show complexity bounds for noisy optimization, in frameworks in which noise is stronger than in previously published papers. We also propose an algorithm based on bandits (variants of ) that reaches the bound within logarithmic factors. We emphasize the differences with empirical derived published algorithms.