Szczegóły publikacji
Opis bibliograficzny
SA-DCGP: Surrogate-Assisted Cartesian Genetic Programming with dynamic operator scheduling for contrastive graph clustering / Maciej KRZYWDA, Szymon ŁUKASIK, Amir H. Gandomi // W: GECCO'26 Companion [Dokument elektroniczny] : proceedings of the 2026 Genetic and Evolutionary Computation Conference : San Jose, Costa Rica, July 13–17, 2026 / eds. Leonardo Trujillo, Ting Hu. — Wersja do Windows. — Dane tekstowe. — New York : Association for Computing Machinery, cop. 2026. — e-ISBN: 979-8-4007-2488-6. — S. 1595–1600. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 1600, Abstr. — Publikacja dostępna online od: 2026-08-13. — Sz. Łukasik - dod. afiliacje: Systems Research Institute Polish Academy of Sciences, Warsaw ; NASK National Research Institute, Warsaw
Autorzy (3)
- AGHKrzywda Maciej
- AGHŁukasik Szymon
- Gandomi Amir H.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169653 |
|---|---|
| Data dodania do BaDAP | 2026-09-29 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3795101.3814678 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | Genetic and Evolutionary Computations 2026 |
Abstract
Neural Architecture Search (NAS) aims to automate the design of neural network architectures, reducing the need for manual expert-driven engineering. Evolutionary approaches, such as Cartesian Genetic Programming (CGP), provide a flexible graph-based representation for evolving neural structures but suffer from high computational costs due to expensive fitness evaluations. In this paper, we propose a Surrogate-Assisted Dynamic Cartesian Genetic Programming (SA-DCGP) framework for automated neural architecture design in graph clustering tasks. The framework evolves architectures composed of FastKAN-based nonlinear blocks and SGCC-style linear normalized layers using dynamic mutation and multiple crossover operators. To reduce evaluation cost, we introduce a pair-wise surrogate model that predicts whether an offspring architecture will outperform its parent based on genotype-derived features and cheap training signals. The surrogate guides selection, enabling full training only for promising candidates. We employ a two-stage evaluation protocol with a cheap training phase for surrogate feature extraction and a full training phase for selected architectures. Clustering performance is evaluated using Accuracy, NMI, ARI, and F1 scores. Experimental results on benchmark graph datasets show that SA-DCGP discovers compact and high-performing architectures while significantly reducing computational overhead, demonstrating the effectiveness of surrogate-assisted evolutionary search for graph-based representation learning.
