Abstract
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.
Cite
CITATION STYLE
Castelli, M., Manzoni, L., Mariot, L., & Saletta, M. (2019). Extending local search in geometric semantic genetic programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11804 LNAI, pp. 775–787). Springer Verlag. https://doi.org/10.1007/978-3-030-30241-2_64
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