---
_id: '1141'
abstract:
- lang: eng
text: In this paper we introduce the Multiobjective Optimization Hierarchic Genetic
Strategy with maturing (MO-mHGS), a meta-algorithm that performs evolutionary
optimization in a hierarchy of populations. The maturing mechanism improves growth
and reduces redundancy. The performance of MO-mHGS with selected state-of-the-art
multiobjective evolutionary algorithms as internal algorithms is analysed on benchmark
problems and their modifications for which single fitness evaluation time depends
on the solution accuracy. We compare the proposed algorithm with the Island Model
Genetic Algorithm as well as with single-deme methods, and discuss the impact
of internal algorithms on the MO-mHGS meta-algorithm. © 2016 Elsevier B.V.
acknowledgement: The work presented in this paper was partially supported by Polish
National Science Centre grant nos. DEC-2012/05/N/ST6/03433 and DEC-2011/03/B/ST6/01393.
Radosław Łazarz was supported by Polish National Science Centre grant no. DEC-2013/10/M/ST6/00531.
author:
- first_name: Radosław
full_name: Łazarz, Radosław
last_name: Łazarz
- first_name: Michał
full_name: Idzik, Michał
last_name: Idzik
- first_name: Konrad
full_name: Gądek, Konrad
last_name: Gądek
- first_name: Ewa P
full_name: Gajda-Zagorska, Ewa P
id: 47794CF0-F248-11E8-B48F-1D18A9856A87
last_name: Gajda-Zagorska
citation:
ama: Łazarz R, Idzik M, Gądek K, Gajda-Zagorska EP. Hierarchic genetic strategy
with maturing as a generic tool for multiobjective optimization. Journal of
Computational Science. 2016;17(1):249-260. doi:10.1016/j.jocs.2016.03.004
apa: Łazarz, R., Idzik, M., Gądek, K., & Gajda-Zagorska, E. P. (2016). Hierarchic
genetic strategy with maturing as a generic tool for multiobjective optimization.
Journal of Computational Science. Elsevier. https://doi.org/10.1016/j.jocs.2016.03.004
chicago: Łazarz, Radosław, Michał Idzik, Konrad Gądek, and Ewa P Gajda-Zagorska.
“Hierarchic Genetic Strategy with Maturing as a Generic Tool for Multiobjective
Optimization.” Journal of Computational Science. Elsevier, 2016. https://doi.org/10.1016/j.jocs.2016.03.004.
ieee: R. Łazarz, M. Idzik, K. Gądek, and E. P. Gajda-Zagorska, “Hierarchic genetic
strategy with maturing as a generic tool for multiobjective optimization,” Journal
of Computational Science, vol. 17, no. 1. Elsevier, pp. 249–260, 2016.
ista: Łazarz R, Idzik M, Gądek K, Gajda-Zagorska EP. 2016. Hierarchic genetic strategy
with maturing as a generic tool for multiobjective optimization. Journal of Computational
Science. 17(1), 249–260.
mla: Łazarz, Radosław, et al. “Hierarchic Genetic Strategy with Maturing as a Generic
Tool for Multiobjective Optimization.” Journal of Computational Science,
vol. 17, no. 1, Elsevier, 2016, pp. 249–60, doi:10.1016/j.jocs.2016.03.004.
short: R. Łazarz, M. Idzik, K. Gądek, E.P. Gajda-Zagorska, Journal of Computational
Science 17 (2016) 249–260.
date_created: 2018-12-11T11:50:22Z
date_published: 2016-11-01T00:00:00Z
date_updated: 2021-01-12T06:48:35Z
day: '01'
department:
- _id: ChWo
doi: 10.1016/j.jocs.2016.03.004
intvolume: ' 17'
issue: '1'
language:
- iso: eng
month: '11'
oa_version: None
page: 249 - 260
publication: Journal of Computational Science
publication_status: published
publisher: Elsevier
publist_id: '6217'
quality_controlled: '1'
scopus_import: 1
status: public
title: Hierarchic genetic strategy with maturing as a generic tool for multiobjective
optimization
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2016'
...