Evolving differential equations with developmental linear genetic programming and epigenetic hill climbing
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{LaCava:2014:GECCOcomp,
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author = "William {La Cava} and Lee Spector and
Kourosh Danai and Matthew Lackner",
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title = "Evolving differential equations with developmental
linear genetic programming and epigenetic hill
climbing",
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booktitle = "GECCO Comp '14: Proceedings of the 2014 conference
companion on Genetic and evolutionary computation
companion",
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year = "2014",
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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",
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isbn13 = "978-1-4503-2881-4",
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keywords = "genetic algorithms, genetic programming: Poster",
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pages = "141--142",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Vancouver, BC, Canada",
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URL = "http://doi.acm.org/10.1145/2598394.2598491",
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DOI = "doi:10.1145/2598394.2598491",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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