Szczegóły publikacji
Opis bibliograficzny
A deep neural network as a TABU support in solving LABS problem / Dominik ŻUREK, Marcin PIETROŃ, Kamil PIĘTAK, Marek KISIEL-DOROHINICKI // 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. 237–243. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-15
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 140672 |
|---|---|
| Data dodania do BaDAP | 2022-09-12 |
| DOI | 10.1007/978-3-031-08754-7_32 |
| 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
One of the leading approaches for solving various hard discrete problems is designing advanced solvers based on local search heuristics. This observation is also relevant to the low autocorrelation binary sequence (LABS) – an open hard optimisation problem that has many applications. There are a lot of dedicated heuristics such as the steepest-descent local search algorithm (SDLS), Tabu search or xLostovka algorithms. This paper introduce a new concept of combining well-known solvers with neural networks that improve the solvers’ parameters based on the local context. The contribution proposes the extension of Tabu search (one of the well-known optimisation heuristics) with the LSTM neural network to optimise the number of iterations for which particular bits are blocked. Regarding the presented results, it should be concluded that the proposed approach is a very promising direction for developing highly efficient heuristics for LABS problem.