Multi-point search algorithm using reinforcement learning

Y. Kobayashi; E. Aiyoshi

Multi-point search algorithm using reinforcement learning

Y. Kobayashi, E. Aiyoshi

Department of Applied Physics and Physico-Informatics

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	3641-3644
Number of pages	4
Publication status	Published - 2005 Dec 1
Event	SICE Annual Conference 2005 - Okayama, Japan Duration: 2005 Aug 8 → 2005 Aug 10

Other

Other	SICE Annual Conference 2005
Country/Territory	Japan
City	Okayama
Period	05/8/8 → 05/8/10

Keywords

Meta-heuristic
Optimization
Steepest Decent Method

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Cite this

@conference{660aa375058042e09d3872f05d11e0f9,

title = "Multi-point search algorithm using reinforcement learning",

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.",

keywords = "Meta-heuristic, Optimization, Steepest Decent Method",

author = "Y. Kobayashi and E. Aiyoshi",

year = "2005",

month = dec,

day = "1",

language = "English",

pages = "3641--3644",

note = "SICE Annual Conference 2005 ; Conference date: 08-08-2005 Through 10-08-2005",

}

TY - CONF

T1 - Multi-point search algorithm using reinforcement learning

AU - Kobayashi, Y.

AU - Aiyoshi, E.

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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.

AB - 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.

KW - Meta-heuristic

KW - Optimization

KW - Steepest Decent Method

UR - http://www.scopus.com/inward/record.url?scp=33645304599&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33645304599&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:33645304599

SP - 3641

EP - 3644

T2 - SICE Annual Conference 2005

Y2 - 8 August 2005 through 10 August 2005

ER -

Multi-point search algorithm using reinforcement learning

Abstract

Other

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this