Geometric semantic genetic programming with local search

Mauro Castelli, Sara Silva, Leonardo Trujillo, Emigdio Z-Flores, Leonardo Vanneschi, Pierrick Legrand

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Citations (Scopus)


Since its introduction, Geometric Semantic Genetic Programming (GSGP) has aroused the interest of numerous researchers and several studies have demonstrated that GSGP is able to effectively optimize training data by means of small variation steps, that also have the effect of limiting overfitting. In order to speed up the search process, in this paper we propose a system that integrates a local search strategy into GSGP (called GSGP-LS). Furthermore, we present a hybrid approach, that combines GSGP and GSGP-LS, aimed at exploiting both the optimization speed of GSGP-LS and the ability to limit overfitting of GSGP. The experimental results we present, performed on a set of complex real-life applications, show that GSGP-LS achieves the best training fitness while converging very quickly, but severely overfits. On the other hand, GSGP converges slowly relative to the other methods, but is basically not affected by overfitting. The best overall results were achieved with the hybrid approach, allowing the search to converge quickly, while also exhibiting a noteworthy ability to limit overfitting. These results are encouraging, and suggest that future GSGP algorithms should focus on finding the correct balance between the greedy optimization of a local search strategy and the more robust geometric semantic operators.

Original languageEnglish
Title of host publicationGECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages8
ISBN (Electronic)9781450334723
Publication statusPublished - 11 Jul 2015
Event16th Genetic and Evolutionary Computation Conference, GECCO 2015 - Madrid, Spain
Duration: 11 Jul 201515 Jul 2015


Conference16th Genetic and Evolutionary Computation Conference, GECCO 2015


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