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

摘要

We show complexity bounds for noisy optimization, in frameworks 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 differences with empirical derived published algorithms. Complete mathematical proofs can be found in [26].