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