Parameter evaluation of geometric semantic genetic programming in pharmacokinetics
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gp-bibliography.bib Revision:1.8051
- @Article{Castelli:2016:IJBIC,
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title = "Parameter evaluation of geometric semantic genetic
programming in pharmacokinetics",
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author = "Mauro Castelli and Leonardo Vanneschi and
Ales Popovic",
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journal = "Int. J. of Bio-Inspired Computation",
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year = "2016",
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month = feb # "~10",
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volume = "8",
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number = "1",
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pages = "42--50",
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keywords = "genetic algorithms, genetic programming, semantics,
geometric semantic operators, regression, parameter
evaluation, pharmacokinetics, semantic crossover,
semantic mutation, drug discovery",
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publisher = "Inderscience Publishers",
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ISSN = "1758-0374",
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bibsource = "OAI-PMH server at www.inderscience.com",
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language = "eng",
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URL = "http://www.inderscience.com/link.php?id=74634",
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DOI = "DOI:10.1504/IJBIC.2016.074634",
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abstract = "The role of crossover and mutation in genetic
programming has been the subject of much debate since
the emergence of the field. Recently new genetic
operators, called geometric semantic operators, have
been introduced. Contrary to standard genetic
operators, these operators present the interesting
property of inducing a unimodal fitness landscape for
every problem that consists in finding a match between
inputs and targets. As the definition of these
operators is quite recent, their effect on the
evolutionary dynamics is still in many senses unknown
and deserves to be studied. This paper intends to fill
this gap, with a specific focus on applications in the
field of pharmacokinetic. Results show that a mixture
of semantic crossover and mutation is always beneficial
compared to the use of only one of these operators.
Furthermore, we show that the best results are obtained
using values of the semantic mutation rate which are
considerably higher than the ones that are typically
used when traditional subtree mutation is employed.",
- }
Genetic Programming entries for
Mauro Castelli
Leonardo Vanneschi
Ales Popovic
Citations