On improving grammatical evolution performance in symbolic regression with attribute grammar

Created by W.Langdon from gp-bibliography.bib Revision:1.7642

@InProceedings{Karim:2014:GECCOcomp,

• author = "Muhammad Rezaul Karim and Conor Ryan",
• title = "On improving grammatical evolution performance in symbolic regression with attribute grammar",
• 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, grammatical evolution: Poster",
• pages = "139--140",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2598394.2598488",
• DOI = "doi:10.1145/2598394.2598488",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "This paper shows how attribute grammar (AG) can be used with Grammatical Evolution (GE) to avoid invalidators in the symbolic regression solutions generated by GE. In this paper, we also show how interval arithmetic can be implemented with AG to avoid selection of certain arithmetic operators or transcendental functions, whenever necessary to avoid infinite output bounds in the solutions. Results and analysis demonstrate that with the proposed extensions, GE shows significantly less overfitting than standard GE and Koza's GP, on the tested symbolic regression problems.",
• notes = "Also known as \cite{2598488} Distributed at GECCO-2014.",

}

Genetic Programming entries for Muhammad Rezaul Karim Conor Ryan

Citations