On the huge benefit of quasi-random mutations for multimodal optimization with application to grid-based tuning of neurocontrollers - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2009

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

Résumé

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).
Fichier principal
Vignette du fichier
qrll.pdf (112.67 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

inria-00380125 , version 1 (30-04-2009)

Identifiants

  • HAL Id : inria-00380125 , version 1

Citer

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. ESANN, Apr 2009, Bruges, Belgium. ⟨inria-00380125⟩
659 Consultations
342 Téléchargements

Partager

Gmail Facebook X LinkedIn More