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@inproceedings{schoenauer:inria00625855,
hal_id = {inria00625855},
url = {http://hal.inria.fr/inria00625855},
title = {{A Rigorous Runtime Analysis for QuasiRandom Restarts and Decreasing Stepsize}},
author = {Schoenauer, Marc and Teytaud, Fabien and Teytaud, Olivier},
abstract = {{MultiModal Optimization (MMO) is ubiquitous in engineer ing, machine learning and artificial intelligence applications. Many algo rithms have been proposed for multimodal optimization, and many of them are based on restart strategies. However, only few works address the issue of initialization in restarts. Furthermore, very few comparisons have been done, between different MMO algorithms, and against simple baseline methods. This paper proposes an analysis of restart strategies, and provides a restart strategy for any local search algorithm for which theoretical guarantees are derived. This restart strategy is to decrease some 'stepsize', rather than to increase the population size, and it uses quasirandom initialization, that leads to a rigorous proof of improve ment with respect to random restarts or restarts with constant initial stepsize. Furthermore, when this strategy encapsulates a (1+1)ES with 1/5th adaptation rule, the resulting algorithm outperforms state of the art MMO algorithms while being computationally faster.}},
language = {Anglais},
affiliation = {TAO  INRIA Saclay  Ile de France , Microsoft Research  Inria Joint Centre  MSR  INRIA , Laboratoire de Recherche en Informatique  LRI},
booktitle = {{Artificial Evolution}},
address = {Angers, France},
audience = {internationale },
year = {2011},
month = Oct,
pdf = {http://hal.inria.fr/inria00625855/PDF/qrrsEA.pdf},
}
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