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Book Sections Year : 2011

Lower Bounds for Evolution Strategies


The mathematical analysis of optimization algorithms involves upper and lower bounds; we here focus on the second case. Whereas other chap- ters will consider black box complexity, we will here consider complexity based on the key assumption that the only information available on the fitness values is the rank of individuals - we will not make use of the exact fitness values. Such a reduced information is known efficient in terms of ro- bustness (Gelly et al., 2007), what gives a solid theoretical foundation to the robustness of evolution strategies, which is often argued without mathemat- ical rigor - and we here show the implications of this reduced information on convergence rates. In particular, our bounds are proved without infi- nite dimension assumption, and they have been used since that time for designing algorithms with better performance in the parallel setting.
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inria-00593179 , version 1 (13-05-2011)
inria-00593179 , version 2 (22-07-2011)


  • HAL Id : inria-00593179 , version 2


Olivier Teytaud. Lower Bounds for Evolution Strategies. Anne Auger, Benjamin Doerr. Theory of Randomized Search Heuristics, 1, World Scientific, pp.327-354, 2011, Series on Theoretical Computer Science. ⟨inria-00593179v2⟩
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