Multi-point search algorithm using reinforcement learning

Y. Kobayashi, E. Aiyoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new meta-heuristic algorithm using multi-point searches and reinforcement learning. The meta-heuristic algorithm has a layered structure that consists of a global search operation and a local search operation for every single point. The results of the local searches are used as the initial conditions of the global search. We demonstrate that the meta-heuristic algorithm with reinforcement learning can efficiently optimize various functions by applying this algorithm to some famous benchmark functions.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages3641-3644
Number of pages4
Publication statusPublished - 2005
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10

Other

OtherSICE Annual Conference 2005
CountryJapan
CityOkayama
Period05/8/805/8/10

Fingerprint

Reinforcement learning
Heuristic algorithms

Keywords

  • Meta-heuristic
  • Optimization
  • Steepest Decent Method

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Kobayashi, Y., & Aiyoshi, E. (2005). Multi-point search algorithm using reinforcement learning. In Proceedings of the SICE Annual Conference (pp. 3641-3644)

Multi-point search algorithm using reinforcement learning. / Kobayashi, Y.; Aiyoshi, E.

Proceedings of the SICE Annual Conference. 2005. p. 3641-3644.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kobayashi, Y & Aiyoshi, E 2005, Multi-point search algorithm using reinforcement learning. in Proceedings of the SICE Annual Conference. pp. 3641-3644, SICE Annual Conference 2005, Okayama, Japan, 05/8/8.
Kobayashi Y, Aiyoshi E. Multi-point search algorithm using reinforcement learning. In Proceedings of the SICE Annual Conference. 2005. p. 3641-3644
Kobayashi, Y. ; Aiyoshi, E. / Multi-point search algorithm using reinforcement learning. Proceedings of the SICE Annual Conference. 2005. pp. 3641-3644
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