Designing graph neural networks (GNNs) for node classification still involves many manual choices. We study a probabilistic L-system-inspired encoding for architecture search, where each individual is a small set of production rules and repeated rewriting generates a symbol sequence that is translated into a GNN. Instead of evolving a fixed layer sequence directly, evolution optimizes the rule contents, their probabilities, the rewriting depth, and a few global architectural parameters. The derived sequences may contain graph message-passing operators, normalization, activation, dropout, and structural components such as Jumping Knowledge. We evaluate the method on nine node-classification benchmarks under multiple search settings that vary selection, crossover, and mutation. The results show that a compact rule-based representation can discover competitive architectures across different graph types while keeping the final model easy to inspect at the rule level.
Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi· Proceedings of the Genetic a...· 0 citations
Designing effective graph neural networks for node classification still depends heavily on human intuition, repeated trial-and-error, and expert knowledge of architectural components. To reduce this manual burden, we introduce a self-evolving neural architecture search framework inspired by Neural Cellular Automata (NCA), in which a population of NCA-based controllers autonomously generates and improves candidate GNN architectures over successive generations. Each controller encodes layer-wise architectural decisions, including layer activation, message-passing operator type, and hidden dimensionality, thereby defining a dynamic search space of graph models. The sampled architectures are trained and evaluated on node classification tasks, while their quality is assessed through a fitness function that jointly considers validation accuracy, validation loss, architectural depth, and parameter count relative to a baseline model. In this way, the proposed framework promotes not only predictive performance but also structural efficiency. Evolution proceeds through elitist retention, tournament selection, and mutation of controller parameters, enabling progressive adaptation of the architecture distribution without hand-crafted design rules.
Maciej Krzywda, M. Wermiński, Szymon Łukasik et al.· Proceedings of the Genetic a...· 0 citations
A pairwise surrogate model is introduced that predicts whether an offspring architecture is likely to outperform its parent using genotype-derived features and early training signals, enabling full evaluation only for promising candidates within the evolutionary loop.
Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi· Proceedings of the Genetic a...· 0 citations
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