An Efficient Implementation of Geometric Semantic Genetic Programming for Anticoagulation Level Prediction in Pharmacogenetics
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Castelli:2013:EPIA,
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author = "Mauro Castelli and Davide Castaldi and
Ilaria Giordani and Sara Silva and Leonardo Vanneschi and
Francesco Archetti and Daniele Maccagnola",
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title = "An Efficient Implementation of Geometric Semantic
Genetic Programming for Anticoagulation Level
Prediction in Pharmacogenetics",
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booktitle = "Proceedings of the 16th Portuguese Conference on
Artificial Intelligence, EPIA 2013",
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year = "2013",
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editor = "Luis Correia and Luis Paulo Reis and Jose Cascalho",
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volume = "8154",
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series = "Lecture Notes in Computer Science",
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pages = "78--89",
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address = "Angra do Heroismo, Azores, Portugal",
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month = sep # " 9-12",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-40668-3",
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URL = "http://link.springer.com/chapter/10.1007/978-3-642-40669-0_8",
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DOI = "doi:10.1007/978-3-642-40669-0_8",
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size = "12 pages",
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abstract = "The purpose of this study is to develop an innovative
system for Coumarin-derived drug dosing, suitable for
elderly patients. Recent research highlights that the
pharmacological response of the patient is often
affected by many exogenous factors other than the
dosage prescribed and these factors could form a very
complex relationship with the drug dosage. For this
reason, new powerful computational tools are needed for
approaching this problem. The system we propose is
called Geometric Semantic Genetic Programming, and it
is based on the use of recently defined geometric
semantic genetic operators. In this paper, we present a
new implementation of this Genetic Programming system,
that allow us to use it for real-life applications in
an efficient way, something that was impossible using
the original definition. Experimental results show the
suitability of the proposed system for managing
anticoagulation therapy. In particular, results
obtained with Geometric Semantic Genetic Programming
are significantly better than the ones produced by
standard Genetic Programming both on training and on
out-of-sample test data.",
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notes = "See \cite{Castelli:2013:GECCOcomp}",
- }
Genetic Programming entries for
Mauro Castelli
Davide Castaldi
Ilaria Giordani
Sara Silva
Leonardo Vanneschi
Francesco Archetti
Daniele Maccagnola
Citations