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.
Maciej Krzywda, Szymon Łukasik, Amir H. Gandomi· GECCO Companion· 0 citations
A self-evolving neural architecture search framework inspired by NCA, in which a population of NCA-based controllers autonomously generates and improves candidate GNN architectures over successive generations, enabling progressive adaptation of the architecture distribution without hand-crafted design rules.
Maciej Krzywda, M. Wermiński, Szymon Łukasik et al.· GECCO Companion· 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· GECCO Companion· 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
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