HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

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
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 TANC - Algorithmic number theory for cryptology
Inria Saclay - Ile de France, LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau]
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).
Complete list of metadata

Cited literature [23 references]  Display  Hide  Download

https://hal.inria.fr/inria-00380125
Contributor : Fabien Teytaud Connect in order to contact the contributor
Submitted on : Thursday, April 30, 2009 - 9:00:21 AM
Last modification on : Thursday, July 8, 2021 - 3:48:37 AM
Long-term archiving on: : Thursday, June 10, 2010 - 6:51:53 PM

File

qrll.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00380125, version 1

Citation

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⟩

Share

Metrics

Record views

647

Files downloads

315