On the analysis of hyper-parameter space for a genetic programming system with iterated F-Race
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{DBLP:journals/soco/TrujilloGGTP20,
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author = "Leonardo Trujillo and Ernesto {Alvarez Gonzalez} and
Edgar Galvan and Juan J. Tapia and Antonin Ponsich",
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title = "On the analysis of hyper-parameter space for a genetic
programming system with iterated {F-Race}",
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journal = "Soft Computing",
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volume = "24",
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number = "19",
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pages = "14757--14770",
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year = "2020",
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month = oct,
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keywords = "genetic algorithms, genetic programming,
Hyper-parameter optimisation, Iterated F-Race",
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URL = "https://doi.org/10.1007/s00500-020-04829-4",
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DOI = "doi:10.1007/s00500-020-04829-4",
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timestamp = "Sat, 19 Sep 2020 01:00:00 +0200",
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biburl = "https://dblp.org/rec/journals/soco/TrujilloGGTP20.bib",
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bibsource = "dblp computer science bibliography, https://dblp.org",
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size = "14 pages",
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abstract = "Evolutionary algorithms (EAs) have been with us for
several decades and are highly popular given that they
have proved competitive in the face of challenging
problems features such as deceptiveness, multiple local
optima, among other characteristics. However, it is
necessary to define multiple hyper-parameter values to
have a working EA, which is a drawback for many
practitioners. In the case of genetic programming (GP),
an EA for the evolution of models and programs,
hyper-parameter optimization has been extensively
studied only recently. This work builds on recent
findings and explores the hyper-parameter space of a
specific GP system called neat-GP that controls model
size. This is conducted using two large sets of
symbolic regression benchmark problems to evaluate
system performance, while hyper-parameter optimization
is carried out using three variants of the iterated
F-Race algorithm, for the first time applied to GP.
From all the automatic parametrisations produced by
optimization process, several findings are drawn.
Automatic parametrizations do not outperform the manual
configuration in many cases, and overall, the
differences are not substantial in terms of testing
error. Moreover, finding parametrisations that produce
highly accurate models that are also compact is not
trivially done, at least if the hyper-parameter
optimization process (F-Race) is only guided by
predictive error. This work is intended to foster more
research and scrutiny of hyper-parameters in EAs, in
general, and GP, in particular.",
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notes = "Tecnologico Nacional de Mexico/IT de Tijuana, Tijuana,
BC, Mexico",
- }
Genetic Programming entries for
Leonardo Trujillo
Ernesto Alvarez Gonzalez
Edgar Galvan Lopez
Juan Jose Tapia Armenta
Antonin Sebastien Ponsich
Citations