Comparison of linear genetic programming variants for symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Sotto:2014:GECCOcomp,

• author = "Leo Francoso Dal Piccol Sotto and Vinicius Veloso {de Melo}",
• title = "Comparison of linear genetic programming variants for symbolic regression",
• 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 = "135--136",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2598394.2598472",
• DOI = "doi:10.1145/2598394.2598472",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "In this paper, we compare a basic linear genetic programming (LGP) algorithm against several LGP variants, proposed by us, on two sets of symbolic regression benchmarks. We evaluated the influence of methods to control bloat, investigated these techniques focused in growth of effective code, and examined an operator to consider two successful individuals as modules to be integrated into a new individual. Results suggest that methods that deal with program size, percentage of effective code, and subfunctions, can improve the quality of the final solutions.",
• notes = "Also known as \cite{2598472} Distributed at GECCO-2014.",

}

Genetic Programming entries for Leo Francoso Dal Piccol Sotto Vinicius Veloso de Melo

Citations