Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning

Yang Chen; Jinglu Hu; Kotaro Hirasawa; Songnian Yu

doi:10.1109/SICE.2007.4421191

Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning

Yang Chen^*, Jinglu Hu, Kotaro Hirasawa, Songnian Yu

^*Corresponding author for this work

Graduate School of Information, Production and Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

8 Citations (Scopus)

Abstract

Recently, an improved genetic algorithm with a reserve selection mechanism (GARS) has been proposed to prevent premature convergence, where a parameter called reserve size plays an important role in optimization performance. In this paper, we propose an approach to the learning of an optimal reserve size in GARS based on the technique of reinforcement learning, where the learning model and algorithm are presented respectively. The experimental results demonstrate the effectiveness of learning algorithm in discovering the optimal reserve size accurately and efficiently.

Original language	English
Title of host publication	SICE Annual Conference, SICE 2007
Pages	1341-1347
Number of pages	7
DOIs	https://doi.org/10.1109/SICE.2007.4421191
Publication status	Published - 2007 Dec 1
Event	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan Duration: 2007 Sept 17 → 2007 Sept 20

Publication series

Name	Proceedings of the SICE Annual Conference

Conference

Conference	SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
Country/Territory	Japan
City	Takamatsu
Period	07/9/17 → 07/9/20

Keywords

Genetic algorithms
Global optimization
Population diversity
Premature convergence
Reinforcement learning
Reserve selection

ASJC Scopus subject areas

Control and Systems Engineering
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/SICE.2007.4421191

Cite this

Chen, Y, Hu, J, Hirasawa, K & Yu, S 2007, Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning. in SICE Annual Conference, SICE 2007., 4421191, Proceedings of the SICE Annual Conference, pp. 1341-1347, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, Japan, 07/9/17. https://doi.org/10.1109/SICE.2007.4421191

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abstract = "Recently, an improved genetic algorithm with a reserve selection mechanism (GARS) has been proposed to prevent premature convergence, where a parameter called reserve size plays an important role in optimization performance. In this paper, we propose an approach to the learning of an optimal reserve size in GARS based on the technique of reinforcement learning, where the learning model and algorithm are presented respectively. The experimental results demonstrate the effectiveness of learning algorithm in discovering the optimal reserve size accurately and efficiently.",

keywords = "Genetic algorithms, Global optimization, Population diversity, Premature convergence, Reinforcement learning, Reserve selection",

author = "Yang Chen and Jinglu Hu and Kotaro Hirasawa and Songnian Yu",

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Y1 - 2007/12/1

N2 - Recently, an improved genetic algorithm with a reserve selection mechanism (GARS) has been proposed to prevent premature convergence, where a parameter called reserve size plays an important role in optimization performance. In this paper, we propose an approach to the learning of an optimal reserve size in GARS based on the technique of reinforcement learning, where the learning model and algorithm are presented respectively. The experimental results demonstrate the effectiveness of learning algorithm in discovering the optimal reserve size accurately and efficiently.

AB - Recently, an improved genetic algorithm with a reserve selection mechanism (GARS) has been proposed to prevent premature convergence, where a parameter called reserve size plays an important role in optimization performance. In this paper, we propose an approach to the learning of an optimal reserve size in GARS based on the technique of reinforcement learning, where the learning model and algorithm are presented respectively. The experimental results demonstrate the effectiveness of learning algorithm in discovering the optimal reserve size accurately and efficiently.

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KW - Population diversity

KW - Premature convergence

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KW - Reserve selection

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Optimizing reserve size in genetic algorithms with reserve selection using reinforcement learning

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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