Szczegóły publikacji
Opis bibliograficzny
Hierarchic Genetic Strategy with maturing as a generic tool for Multiobjective Optimization / Radosław ŁAZARZ, Michał IDZIK, Konrad Gądek, Ewa GAJDA-ZAGÓRSKA // Journal of Computational Science ; ISSN 1877-7503. — 2016 — vol. 17 pt. 1, s. 249–260. — Bibliogr. s. 259–260, Abstr. — Publikacja dostępna online od: 2016-03-05. — E. Gajda-Zagórska - dod. afiliacja: Institute of Science and Technology Austria
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 104490 |
|---|---|
| Data dodania do BaDAP | 2017-03-22 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.jocs.2016.03.004 |
| Rok publikacji | 2016 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Journal of Computational Science |
Abstract
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.