Geometric Semantic Genetic Programming with Local Search

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Castelli:2015:GECCO,

• author = "Mauro Castelli and Leonardo Trujillo and Leonardo Vanneschi and Sara Silva and Emigdio Z-Flores and Pierrick Legrand",
• title = "Geometric Semantic Genetic Programming with Local Search",
• booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr",
• isbn13 = "978-1-4503-3472-3",
• pages = "999--1006",
• keywords = "genetic algorithms, genetic programming",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739480.2754795",
• DOI = "doi:10.1145/2739480.2754795",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "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.",
• notes = "Also known as \cite{2754795} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)",

}

Genetic Programming entries for Mauro Castelli Leonardo Trujillo Leonardo Vanneschi Sara Silva Emigdio Z-Flores Pierrick Legrand

Citations