Szczegóły publikacji

Opis bibliograficzny

Evolutionary multi-modal optimization with the use of multi-objective techniques / Leszek SIWIK, Rafał DREŻEWSKI // W: Artificial Intelligence and Soft Computing : 13th International Conference, ICAISC 2014 : Zakopane, Poland, June 1–5, 2014 : proceedings, Pt. 1 / eds. Leszek Rutkowski [et al.]. — Berlin ; Heidelberg : Springer-Verlag, cop. 2014. — (Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; 8467). — ISBN: 978-3-319-07172-5; e-ISBN: 978-3-319-07173-2. — S. 428–439. — Bibliogr. s. 438–439, Abstr.

Autorzy (2)

Dane bibliometryczne

ID BaDAP81915
Data dodania do BaDAP2014-06-24
DOI10.1007/978-3-319-07173-2_37
Rok publikacji2014
Typ publikacjimateriały konferencyjne (aut.)
Otwarty dostęptak
KonferencjaInternational Conference on Artificial Intelligence and Soft Computing 2014
Czasopismo/seriaLecture Notes in Computer Science

Abstract

When evolutionary algorithms for solving multi-modal optimization problems are applied, the crucial issue to be solved is maintaining population diversity to avoid drifting and focusing individuals around single global optima. A lot of techniques have been used here so far. Simultaneously for last twenty years a lot of effort has been made in the area of evolutionary algorithms for multi-objective optimization. As the result at least several highly efficient algorithms have been proposed such as NSGAII or SPEA2. Obviously, also in this case maintaining of population diversity is crucial but this time, taking the specificity of optimization in the Pareto sense, there are built-in mechanisms to solve this issue effectively. If so, the idea arises of applying of state-of-theart evolutionary multi-objective optimization algorithms for solving not original multi-modal (but single-objective) optimization task but rather its transformed into multi-objective problem form by introducing additional dispersion-oriented criteria. The goal of this paper is to present some further study in this area.

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