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Resumo(s)
The introduction of a Cellular Automata (CA)-like structure on the population of Evolutionary Algorithms (EAs) has been verified to be a method to improve solutions quality. However, the study of CA-like structures for Genetic Programming (GP) has been, so far, limited. In this work, we focus on the effect of introducing these structures on Geometric Semantic variants of GP, focusing on the well-known Geometric Semantic GP (GSGP) and its recently introduced variant SLIM-GSGP, which emphasizes producing smaller and more interpretable individuals. Here we provide guidance on how CA-like structures can impact the quality and size of the solutions for GSGP and SLIM-GSGP, giving a clear understanding of the trade-offs involved in applying these methods.
Descrição
Rovito, L., Bonin, L., Farinati, D., Vanneschi, L., Manzoni, L., De Lorenzo, A., & Pietropolli, G. (2025). Exploring the Integration of Cellular Structures in Genetic Programming-Based Methods. In B. Xue, L. Manzoni, & I. Bakurov (Eds.), Genetic Programming: 28th European Conference, EuroGP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings (pp. 120-138). (Lecture Notes in Computer Science; Vol. 15609). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-89991-1_8 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS (https://doi.org/10.54499/UIDB/04152/2020).
This research is partially supported by the PRIN 2022 PNRR project “Cellular Automata Synthesis for Cryptography Applications (CASCA)” (P2022MPFRT) financed by the European Union - Next Generation EU.
Palavras-chave
Evolutionary Computation Evolutionary Algorithms Genetic Programming Geometric Semantic Genetic Programming Cellular Automata Symbolic Regression Theoretical Computer Science General Computer Science
Contexto Educativo
Citação
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Springer Nature Switzerland AG
