Multi-point search algorithm using reinforcement learning

Y. Kobayashi, E. Aiyoshi

Research output: Contribution to conferencePaper

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
Pages3641-3644
Number of pages4
Publication statusPublished - 2005 Dec 1
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10

Other

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

Keywords

  • Meta-heuristic
  • Optimization
  • Steepest Decent Method

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Kobayashi, Y., & Aiyoshi, E. (2005). Multi-point search algorithm using reinforcement learning. 3641-3644. Paper presented at SICE Annual Conference 2005, Okayama, Japan.