An Efficient Genetic Programming System with Geometric Semantic Operators and its Application to Human Oral Bioavailability Prediction
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- @Misc{Castelli:2012:arXiv,
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title = "An Efficient Genetic Programming System with Geometric
Semantic Operators and its Application to Human Oral
Bioavailability Prediction",
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author = "Mauro Castelli and Luca Manzoni and
Leonardo Vanneschi",
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howpublished = "arXiv",
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year = "2012",
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month = "12 " # aug,
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keywords = "genetic algorithms, genetic programming",
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bibdate = "2012-10-10",
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URL = "http://arxiv.org/abs/1208.2437",
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size = "10 pages",
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abstract = "Very recently new genetic operators, called geometric
semantic operators, have been defined for genetic
programming. Contrarily to standard genetic operators,
which are uniquely based on the syntax of the
individuals, these new operators are based on their
semantics, meaning with it the set of input-output
pairs on training data. Furthermore, these operators
present the interesting property of inducing a unimodal
fitness landscape for every problem that consists in
finding a match between given input and output data
(for instance regression and classification).
Nevertheless, the current definition of these operators
has a serious limitation: they impose an exponential
growth in the size of the individuals in the
population, so their use is impossible in practice.
This paper is intended to overcome this limitation,
presenting a new genetic programming system that
implements geometric semantic operators in an extremely
efficient way. To demonstrate the power of the proposed
system, we use it to solve a complex real-life
application in the field of pharmacokinetic: the
prediction of the human oral bioavailability of
potential new drugs. Besides the excellent performances
on training data, which were expected because the
fitness landscape is unimodal, we also report an
excellent generalisation ability of the proposed
system, at least for the studied application. In fact,
it outperforms standard genetic programming and a wide
set of other well-known machine learning methods.",
- }
Genetic Programming entries for
Mauro Castelli
Luca Manzoni
Leonardo Vanneschi
Citations