Szczegóły publikacji
Opis bibliograficzny
Multiwinner voting in genetic algorithms for solving ill-posed global optimization problems / Piotr FALISZEWSKI, Jakub Sawicki, Robert SCHAEFER, Maciej SMOŁKA // W: Applications of evolutionary computation : 19th European Conference, EvoApplications 2016 : Porto, Portugal, March 30–April 1, 2016 : proceedings, Pt. 1 / eds. Giovanni Squillero, Paolo Burelli. — Switzerland : Springer International Publishing, 2016. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 9597). — ISBN: 978-3-319-31203-3; e-ISBN: 978-3-319-31204-0. — S. 409–424. — Bibliogr. s. 423–424, Abstr.
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 103257 |
|---|---|
| Data dodania do BaDAP | 2017-01-20 |
| DOI | 10.1007/978-3-319-31204-0_27 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | 19th European Conference on Applications of Evolutionary Computation |
| Czasopisma/serie | Lecture Notes in Computer Science, Theoretical Computer Science and General Issues |
Abstract
Genetic algorithms are a group of powerful tools for solving ill-posed global optimization problems in continuous domains. In case in which the insensitivity of the fitness function is the main obstacle, the most desired feature of a genetic algorithm is its ability to explore plateaus of the fitness function, surrounding its minimizers. In this paper we suggest a way of maintaining diversity of the population in the plateau regions, based on a new approach for the selection based on the theory of multiwinner elections among autonomous agents. The paper delivers a detailed description of the new selection algorithm, computational experiments that guide the choice of the proper multiwinner rule to use, and a preliminary experiment showing the proposed algorithm’s effectiveness in exploring a fitness function’s plateau.