Szczegóły publikacji
Opis bibliograficzny
Socio-cognitive agent-oriented evolutionary algorithm with trust-based optimization / Aleksandra URBAŃCZYK, Krzysztof Czech, Piotr URBAŃCZYK, Marek KISIEL-DOROHINICKI, Aleksander BYRSKI // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings , Pt. 1 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — ( Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15907 ). — ISBN: 978-3-031-97553-0; e-ISBN: 978-3-031-97554-7. — S. 249–264. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-07. — P. Urbańczyk - dod. afiliacja: Jagiellonian University
Autorzy (5)
Dane bibliometryczne
| ID BaDAP | 161030 |
|---|---|
| Data dodania do BaDAP | 2025-08-07 |
| DOI | 10.1007/978-3-031-97554-7_18 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This paper introduces the Trust-Based Optimization (TBO), a novel extension of the island model in evolutionary computation that replaces conventional periodic migrations with a flexible, agent-driven interaction mechanism based on trust or reputation. Experimental results demonstrate that TBO generally outperforms the standard island model evolutionary algorithm across various optimization problems. Nevertheless, algorithm performance varies depending on the problem type, with certain configurations being more effective for specific landscapes or dimensions. The findings suggest that trust and reputation mechanisms provide a flexible and adaptive approach to evolutionary optimization, improving solution quality in many cases.
