Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization Journal Article


Author(s): Łazarz, Radosław; Idzik, Michał; Gądek, Konrad; Gajda-Zagórska, Ewa
Article Title: Hierarchic genetic strategy with maturing as a generic tool for multiobjective optimization
Affiliation IST Austria
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. © 2016 Elsevier B.V.
Keywords: Adaptive metaheuristic; Hierarchic evolutionary algorithm; Multiobjective Optimization
Journal Title: Journal of Computational Science
Volume: 17
ISSN: 18777503
Publisher: Elsevier  
Date Published: 2016-11-01
Start Page: 249
End Page: 260
DOI: 10.1016/j.jocs.2016.03.004
Notes: 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.
Open access: no