Localization strategy for island model genetic algorithm to preserve population diversity

Alfian Akbar Gozali, Shigeru Fujimura

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

Years after being firstly introduced by Fraser and remodeled for modern application by Bremermann, genetic algorithm (GA) has a significant progression to solve many kinds of optimization problems. GA also thrives into many variations of models and approaches. Multi-population or island model GA (IMGA) is one of the commonly used GA models. IMGA is a multi-population GA model objected to getting a better result (aimed to get global optimum) by intrinsically preserve its diversity. Localization strategy of IMGA is a new approach which sees an island as a single living environment for its individuals. An island’s characteristic must be different compared to other islands. Operator parameter configuration or even its core engine (algorithm) represents the nature of an island. These differences will incline into different evolution tracks which can be its speed or pattern. Localization strategy for IMGA uses three kinds of single GA core: standard GA, pseudo GA, and informed GA. Localization strategy implements migration protocol and the bias value to control the movement. The experiment results showed that localization strategy for IMGA succeeds to solve 3-SAT with an excellent performance. This brand new approach is also proven to have a high consistency and durability.

本文言語English
ホスト出版物のタイトルComputer and Information Science
編集者Roger Lee
出版社Springer Verlag
ページ149-161
ページ数13
ISBN(印刷版)9783319601694
DOI
出版ステータスPublished - 2018
イベント16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China
継続期間: 2017 5 242017 5 26

出版物シリーズ

名前Studies in Computational Intelligence
719
ISSN(印刷版)1860-949X

Other

Other16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017
国/地域China
CityWuhan
Period17/5/2417/5/26

ASJC Scopus subject areas

  • 人工知能

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