Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{LaCava:2014:GECCOcomp,

• author = "William {La Cava} and Lee Spector and Kourosh Danai and Matthew Lackner",
• title = "Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing",
• 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 = "141--142",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2598394.2598491",
• DOI = "doi:10.1145/2598394.2598491",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "This paper describes a method of solving the symbolic regression problem using developmental linear genetic programming (DLGP) with an epigenetic hill climber (EHC). We propose the EHC for optimising the epigenetic properties of the genotype. The epigenetic characteristics are then inherited through coevolution with the population. Results reveal that the EHC improves performance through maintenance of smaller expressed program sizes. For some problems it produces more successful runs while remaining essentially cost-neutral with respect to number of fitness evaluations.",
• notes = "Also known as \cite{2598491} Distributed at GECCO-2014.",

}

Genetic Programming entries for William La Cava Lee Spector Kourosh Danai Matthew A Lackner

Citations