Created by W.Langdon from gp-bibliography.bib Revision:1.2031
@InProceedings{mcphee:2009:gecco,
author = "Nicholas Freitag McPhee and Ellery Fussell Crane and
Sara E. Lahr and Riccardo Poli",
title = "Developmental plasticity in linear genetic
programming",
booktitle = "GECCO '09: Proceedings of the 11th Annual conference
on Genetic and evolutionary computation",
year = "2009",
editor = "Guenther Raidl and Franz Rothlauf and
Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and
Mauro Birattari and Clare Bates Congdon and
Martin Middendorf and Christian Blum and Carlos Cotta and
Peter Bosman and Joern Grahl and Joshua Knowles and
David Corne and Hans-Georg Beyer and Ken Stanley and
Julian F. Miller and Jano {van Hemert} and
Tom Lenaerts and Marc Ebner and Jaume Bacardit and
Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and
Thomas Jansen and Riccardo Poli and Enrique Alba",
pages = "1019--1026",
address = "Montreal",
month = "8-12 " # jul,
organisation = "SigEvo",
publisher = "ACM",
publisher_address = "New York, NY, USA",
note = "Nominated for best paper award in the GP track",
keywords = "genetic algorithms, genetic programming, EDA",
isbn13 = "978-1-60558-325-9",
doi = "
doi:10.1145/1569901.1570039",
size = "8 pages",
abstract = "Biological organisms exhibit numerous types of
plasticity, where they respond both developmentally and
behaviorally to environmental factors. In some
organisms, for example, environmental conditions can
lead to the developmental expression of genes that
would otherwise remain dormant, leading to significant
phenotypic variation and allowing selection to act on
these otherwise {"}invisible{"} genes. In contrast to
biological plasticity, the vast majority of
evolutionary computation systems, including genetic
programming, are rigid and can only adapt to very
limited external changes. In this paper we extend the
N-gram GP system, a recently introduced estimation of
distribution algorithm for program evolution, using
Incremental Fitness-based Development (IFD), a novel
technique which allows for developmental plasticity in
the generation of linear-GP style programs. Tests with
a large set of problems show that the new system
outperforms the original N-gram GP system and is
competitive with standard GP. Analysis of the evolved
programs indicates that IFD allows for the generation
of more complex programs than standard N-gram GP, with
the generated programs often containing several
separate sequences of instructions that are reused
multiple times, often with variations.",
notes = "n-gram GP, 3 gram linear GP. Removed 2nd and first
order derived matrices by starting program with two
null operations. Program increases size by greedy
addition of blocks on instructions. Incremental blocks
need not be three instruction long. Symbolic
regression. Additional blocks added at random until
fitness is improved or time out.
One fixed (protected?) input register. ROUT different.
memory with memory. {"}Does not scale well to large
pools of constants{"} {"}IFD never hurts{"}
IFD solutions both {"}more complex{"} and {"}mode
modular{"}
GECCO-2009 A joint meeting of the eighteenth
international conference on genetic algorithms
(ICGA-2009) and the fourteenth annual genetic
programming conference (GP-2009).
ACM Order Number 910092. Also known as
\cite{DBLP:conf/gecco/McPheeCLP09}",
}
Genetic Programming entries for Nicholas Freitag McPhee Ellery Fussell Crane Sara E Lahr Riccardo Poli