Szczegóły publikacji
Opis bibliograficzny
Enhancing Grammatical Evolution with neural-inspired CFG for Explainable AI / Jakub SKRZYŃSKI, Antoni LIGĘZA // W: Advances in artificial intelligence research : proceedings of the 6th Polish conference on Artificial Intelligence, PP-RAI 2025, Katowice, Poland, 7–9 April, 2025 / eds. Beata Zielosko, [et al.]. — Cham : Springer Nature Swizterland, cop. 2026. — ( Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; vol. 1599 ). — ISBN: 978-3-032-04196-8; e-ISBN: 978-3-032-04197-5. — S. 17–29. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-01-02
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 165508 |
|---|---|
| Data dodania do BaDAP | 2026-01-21 |
| DOI | 10.1007/978-3-032-04197-5_2 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
Abstract
This paper explores the use of extended grammars in Grammatical Evolution (GE) to solve complex problems while maintaining interpretability. By incorporating external knowledge through custom-defined grammars, the proposed approach aims to generate solutions that are both accurate and explainable. Two new grammars are introduced: one liberal and one strict, which enhance GE by incorporating neural network-inspired structures. These grammars include activation functions commonly used in neural networks and allow the construction of models that mimic neural network behavior without the need for training. Experiments conducted on various datasets demonstrate that the use of these grammars leads to improved robustness, accuracy, and model explainability compared to traditional methods. The results suggest that GE can be a powerful tool for building interpretable models that challenge the dominance of black-box approaches, such as neural networks, making it a promising candidate for Explainable AI (XAI) applications. Furthermore, the reproducibility of the experiments is ensured through publicly available Jupyter notebooks and datasets, allowing for the verification of results and further exploration.
