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Publicação
Extending local search in geometric semantic genetic programming
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Orientador(es)
Resumo(s)
In this paper we continue the investigation of the effect of local search in geometric semantic genetic programming (GSGP), with the introduction of a new general local search operator that can be easily customized. We show that it is able to obtain results on par with the current best-performing GSGP with local search and, in most cases, better than standard GSGP.
Descrição
Castelli, M., Manzoni, L., Mariot, L., & Saletta, M. (2019). Extending local search in geometric semantic genetic programming. In P. Moura Oliveira, P. Novais, & L. P. Reis (Eds.), Progress in Artificial Intelligence : 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings (pp. 775-787). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11804 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-30241-2_64
Palavras-chave
Theoretical Computer Science General Computer Science
Contexto Educativo
Citação
Editora
Springer Verlag