Performance analysis of localization strategy for island model genetic algorithm

Alfian Akbar Gozali, Shigeru Fujimura

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

2 Citations (Scopus)

Abstract

Genetic algorithm (GA) is one of the standard solutions to solve many optimization problems. One of a GA type used for solving a case is island model GA (IMGA). Localization strategy is a brand-new feature for IMGA to better preserves its diversity. In the previous research, localization strategy could carry out 3SAT problem almost perfectly. In this study, the proposed feature is aimed to solve real parameter single objective computationally expensive optimization problems. Differ with an issue in previous research which has a prior knowledge and binary, the computationally expensive optimization has not any prior knowledge and floating type problem. Therefore, the localization strategy and its GA cores must adapt. The primary goal of this research is to analyze further the localization strategy for IMGA's performance. The experiments show that the new feature is successfully modified to meet the new requirement. Localization strategy for IMGA can solve all computationally expensive functions consistently. Moreover, this new feature could make IMGA reaches leading ratio 0.47 among other current solvers.

Original languageEnglish
Title of host publicationProceedings - 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017
EditorsHiroaki Hirata, Nomiya Hiroki, Teruhisa Hochin
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages327-332
Number of pages6
ISBN (Electronic)9781509055043
DOIs
Publication statusPublished - 2017 Aug 29
Event18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017 - Kanazawa, Japan
Duration: 2017 Jun 262017 Jun 28

Publication series

NameProceedings - 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017

Other

Other18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017
CountryJapan
CityKanazawa
Period17/6/2617/6/28

Keywords

  • Computationally expensive optimization
  • Genetic algorithms
  • Island model genetic algorithm
  • Localization strategy

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture

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  • Cite this

    Gozali, A. A., & Fujimura, S. (2017). Performance analysis of localization strategy for island model genetic algorithm. In H. Hirata, N. Hiroki, & T. Hochin (Eds.), Proceedings - 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017 (pp. 327-332). [8022741] (Proceedings - 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SNPD.2017.8022741