Neural Architecture Search (NAS) aims to automate the design of neural network architectures, reducing reliance on manual engineering. Evolutionary approaches provide flexible search mechanisms but suffer from high computational cost due to expensive fitness evaluations. In this paper, we propose a Surrogate-Assisted Linear Genetic Programming (SA-LGP) framework for automated design of Graph Neural Network (GNN) architectures for node classification. Unlike conventional NAS methods based on fixed-length or graph-structured encodings, our approach employs variable-length linear genetic programs to construct architectures composed of diverse message-passing layers and regularization operators. To reduce computational cost, we introduce a pairwise surrogate model that predicts whether an offspring architecture is likely to outperform its parent using genotype-derived features and early training signals. The surrogate acts as a filter within the evolutionary loop, enabling full evaluation only for promising candidates. A two-stage evaluation protocol is used, consisting of a cheap early-training phase and a full-training phase for selected architectures. Experiments on benchmark graph datasets show that SA-LGP discovers compact and competitive GNN architectures while significantly reducing the number of expensive evaluations, demonstrating the effectiveness of surrogate-assisted evolutionary search.
Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi· Proceedings of the Genetic a...· 0 citations
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.
Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi· Proceedings of the Genetic a...· 0 citations