Szczegóły publikacji
Opis bibliograficzny
Neural-network based adaptation of variation operators' parameters for metaheuristics / Tymoteusz Dobrzański, Aleksandra URBAŃCZYK, Tomasz PEŁECH-PILICHOWSKI, Marek KISIEL-DOROHINICKI, Aleksander BYRSKI // W: Computational Science – ICCS 2022 : 22nd international conference : London, UK, June 21–23, 2022 : proceedings, Pt. 2 / eds. Derek Groen, [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13351). — ISBN: 978-3-031-08753-0; e-ISBN: 978-3-031-08754-7. — S. 394–407. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-15
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140671 |
|---|---|
| Data dodania do BaDAP | 2022-06-24 |
| DOI | 10.1007/978-3-031-08754-7_47 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 22nd International Conference on Computational Science |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
The paper presents an idea of training an artificial neural network a relation between different parameters observed for a population in a metaheuristic algorithm. Then such trained network may be used for controlling other algorithms (if the network is trained in such way, that the knowledge gathered by it becomes agnostic regarding the problem). The paper focuses on showing the idea and also provides selected experimental results obtained after applying the proposed algorithm for solving popular benchmark problems in different dimensions.