A Re-Examination of the Use of Genetic Programming on the Oral Bioavailability Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Dick:2015:GECCO,
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author = "Grant Dick and Aysha P. Rimoni and Peter A. Whigham",
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title = "A Re-Examination of the Use of Genetic Programming on
the Oral Bioavailability Problem",
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booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference
on Genetic and Evolutionary Computation",
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year = "2015",
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editor = "Sara Silva and Anna I Esparcia-Alcazar and
Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and
Christine Zarges and Luis Correia and Terence Soule and
Mario Giacobini and Ryan Urbanowicz and
Youhei Akimoto and Tobias Glasmachers and
Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and
Marta Soto and Carlos Cotta and Francisco B. Pereira and
Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and
Heike Trautmann and Jean-Baptiste Mouret and
Sebastian Risi and Ernesto Costa and Oliver Schuetze and
Krzysztof Krawiec and Alberto Moraglio and
Julian F. Miller and Pawel Widera and Stefano Cagnoni and
JJ Merelo and Emma Hart and Leonardo Trujillo and
Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and
Carola Doerr",
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isbn13 = "978-1-4503-3472-3",
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pages = "1015--1022",
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keywords = "genetic algorithms, genetic programming",
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month = "11-15 " # jul,
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organisation = "SIGEVO",
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address = "Madrid, Spain",
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URL = "http://doi.acm.org/10.1145/2739480.2754771",
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DOI = "doi:10.1145/2739480.2754771",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Difficult benchmark problems are in increasing demand
in Genetic Programming (GP). One problem seeing
increased usage is the oral bioavailability problem,
which is often presented as a challenging problem to
both GP and other machine learning methods. However,
few properties of the bioavailability data set have
been demonstrated, so attributes that make it a
challenging problem are largely unknown. This work
uncovers important properties of the bioavailability
data set, and suggests that the perceived difficulty in
this problem can be partially attributed to a lack of
pre-processing, including features within the data set
that contain no information, and contradictory
relationships between the dependent and independent
features of the data set. The paper then re-examines
the performance of GP on this data set, and
contextualises this performance relative to other
regression methods. Results suggest that a large
component of the observed performance differences on
the bioavailability data set can be attributed to
variance in the selection of training and testing data.
Differences in performance between GP and other methods
disappear when multiple training/testing splits are
used within experimental work, with performance
typically no better than a null modelling approach of
reporting the mean of the training data.",
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notes = "Also known as \cite{2754771} GECCO-2015 A joint
meeting of the twenty fourth international conference
on genetic algorithms (ICGA-2015) and the twentith
annual genetic programming conference (GP-2015)",
- }
Genetic Programming entries for
Grant Dick
Aysha Rimoni
Peter Alexander Whigham
Citations