SA-DCGP: Surrogate-Assisted Cartesian Genetic Programming with Dynamic Operator Scheduling for Contrastive Graph Clustering
TL;DR
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.
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.