A comparison between geometric semantic GP and cartesian GP for boolean functions learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7917

@InProceedings{Mambrini:2014:GECCOcomp,

• author = "Andrea Mambrini and Luca Manzoni",
• title = "A comparison between geometric semantic GP and cartesian GP for boolean functions learning",
• booktitle = "GECCO Comp '14: Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion",
• year = "2014",
• editor = "Christian Igel and Dirk V. Arnold and Christian Gagne and Elena Popovici and Anne Auger and Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and Kalyanmoy Deb and Benjamin Doerr and James Foster and Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and Hitoshi Iba and Christian Jacob and Thomas Jansen and Yaochu Jin and Marouane Kessentini and Joshua D. Knowles and William B. Langdon and Pedro Larranaga and Sean Luke and Gabriel Luque and John A. W. McCall and Marco A. {Montes de Oca} and Alison Motsinger-Reif and Yew Soon Ong and Michael Palmer and Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and Guenther Ruhe and Tom Schaul and Thomas Schmickl and Bernhard Sendhoff and Kenneth O. Stanley and Thomas Stuetzle and Dirk Thierens and Julian Togelius and Carsten Witt and Christine Zarges",
• isbn13 = "978-1-4503-2881-4",
• keywords = "genetic algorithms, genetic programming: Poster",
• pages = "143--144",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2598394.2598475",
• DOI = "doi:10.1145/2598394.2598475",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Geometric Semantic Genetic Programming (GSGP) is a recently defined form of Genetic Programming (GP) that has shown promising results on single output Boolean problems when compared with standard tree-based GP. In this paper we compare GSGP with Cartesian GP (CGP) on comprehensive set of Boolean benchmarks, consisting of both single and multiple outputs Boolean problems. The results obtained show that GSGP outperforms also CGP, confirming the efficacy of GSGP in solving Boolean problems.",
• notes = "Also known as \cite{2598475} Distributed at GECCO-2014.",

}

Genetic Programming entries for Andrea Mambrini Luca Manzoni

Citations