Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018)

Localized Island Model Genetic Algorithm in Population Diversity Preservation

Authors
Alfian Akbar Gozali, Shigeru Fujimura
Corresponding Author
Alfian Akbar Gozali
Available Online March 2019.
DOI
https://doi.org/10.2991/icoiese-18.2019.22How to use a DOI?
Keywords
migration policy; island model; genetic algorithm; localization strategy
Abstract
Premature convergence in island model is a consequence of the selection in migration mechanism. It is a process of migrating several individuals (usually the best one) from a source into destination island to keep its diversity. The main reason is the similar characteristic of relocated individual because of the genetic operator configurations are similar. Localized Island Model GA (LIMGA) tries to implement different island characteristics (localization strategy) to preserve the islands’ diversity. By harmonizing standard GA, pseudo GA, and informed GA; LIMGA could overcome general optimization problem with a great result and acceptable execution time. Moreover, because of its success in maintaining the diversity, LIMGA could lead the current best-known-so-far solver for this case.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
Part of series
Atlantis Highlights in Engineering
Publication Date
March 2019
ISBN
978-94-6252-689-1
ISSN
2589-4943
DOI
https://doi.org/10.2991/icoiese-18.2019.22How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Alfian Akbar Gozali
AU  - Shigeru Fujimura
PY  - 2019/03
DA  - 2019/03
TI  - Localized Island Model Genetic Algorithm in Population Diversity Preservation
BT  - 2018 International Conference on Industrial Enterprise and System Engineering (ICoIESE 2018)
PB  - Atlantis Press
SN  - 2589-4943
UR  - https://doi.org/10.2991/icoiese-18.2019.22
DO  - https://doi.org/10.2991/icoiese-18.2019.22
ID  - Gozali2019/03
ER  -