On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers

Guillaume Chaslot; Jean-Baptiste Hoock; Fabien Teytaud; Olivier Teytaud

On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers

Guillaume Chaslot ¹ Jean-Baptiste Hoock ² Fabien Teytaud ^{2, 3, 4} Olivier Teytaud ^{2, 3, 4}

2 TAO - Machine Learning and Optimisation

LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623

3 TANC - Algorithmic number theory for cryptology

LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France

4 LRI - Laboratoire de Recherche en Informatique

Abstract : In this paper, we study the optimization of a neural network used for controlling a Monte-Carlo Tree Search (MCTS/UCT) algorithm. The main results are: (i) the specification of a new multimodal benchmark function; this function has been defined in particular in agreement with [1] which has pointed out that most multimodal functions are not satisfactory for some real-world multimodal scenarios (section 2); (ii) experimentation of Evolution Strategies on this new multimodal benchmark function, showing the great efficiency of quasi-random mutations in this framework (section 3); (iii) the proof-of-concept of the application of ES for grid-based tuning Neural Networks for controlling MCTS/UCT (see section 3).

Type de document :

Communication dans un congrès

ESANN, Apr 2009, Bruges, Belgium. 2009

Domaine :

Informatique [cs] / Réseau de neurones [cs.NE]

Mathématiques [math] / Optimisation et contrôle [math.OC]

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https://hal.inria.fr/inria-00380125
Contributeur : Fabien Teytaud <>
Soumis le : jeudi 30 avril 2009 - 09:00:21
Dernière modification le : mercredi 28 novembre 2018 - 15:36:02
Document(s) archivé(s) le : jeudi 10 juin 2010 - 18:51:53

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On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers

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