Localization strategy for island model genetic algorithm to preserve population diversity

Alfian Akbar Gozali, Shigeru Fujimura

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

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationComputer and Information Science
PublisherSpringer Verlag
Pages149-161
Number of pages13
Volume719
ISBN (Print)9783319601694
DOIs
Publication statusPublished - 2018
Event16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017 - Wuhan, China
Duration: 2017 May 242017 May 26

Publication series

NameStudies in Computational Intelligence
Volume719
ISSN (Print)1860-949X

Other

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

Fingerprint

Genetic algorithms
Durability
Engines
Experiments

Keywords

  • 3-SAT
  • Genetic algorithms
  • Island model genetic algorithm
  • Localization strategy

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Gozali, A. A., & Fujimura, S. (2018). Localization strategy for island model genetic algorithm to preserve population diversity. In Computer and Information Science (Vol. 719, pp. 149-161). (Studies in Computational Intelligence; Vol. 719). Springer Verlag. https://doi.org/10.1007/978-3-319-60170-0_11

Localization strategy for island model genetic algorithm to preserve population diversity. / Gozali, Alfian Akbar; Fujimura, Shigeru.

Computer and Information Science. Vol. 719 Springer Verlag, 2018. p. 149-161 (Studies in Computational Intelligence; Vol. 719).

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

Gozali, AA & Fujimura, S 2018, Localization strategy for island model genetic algorithm to preserve population diversity. in Computer and Information Science. vol. 719, Studies in Computational Intelligence, vol. 719, Springer Verlag, pp. 149-161, 16th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2017, Wuhan, China, 17/5/24. https://doi.org/10.1007/978-3-319-60170-0_11
Gozali AA, Fujimura S. Localization strategy for island model genetic algorithm to preserve population diversity. In Computer and Information Science. Vol. 719. Springer Verlag. 2018. p. 149-161. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-319-60170-0_11
Gozali, Alfian Akbar ; Fujimura, Shigeru. / Localization strategy for island model genetic algorithm to preserve population diversity. Computer and Information Science. Vol. 719 Springer Verlag, 2018. pp. 149-161 (Studies in Computational Intelligence).
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