Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement
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- @Article{Castelli:2016:CIN,
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author = "Mauro Castelli and Leonardo Vanneschi and
Ales Popovic",
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title = "Controlling Individuals Growth in Semantic Genetic
Programming through Elitist Replacement",
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journal = "Computational Intelligence and Neuroscience",
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year = "2016",
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pages = "Article ID 8326760",
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keywords = "genetic algorithms, genetic programming",
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publisher = "Hindawi Publishing Corporation",
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bibsource = "OAI-PMH server at www.ncbi.nlm.nih.gov",
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identifier = "/pmc/articles/PMC4707023/",
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language = "en",
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oai = "oai:pubmedcentral.nih.gov:4707023",
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rights = "Copyright 2016 Mauro Castelli et al.; This is an open
access article distributed under the Creative Commons
Attribution License, which permits unrestricted use,
distribution, and reproduction in any medium, provided
the original work is properly cited.",
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URL = "http://dx.doi.org/10.1155/2016/8326760",
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URL = "http://downloads.hindawi.com/journals/cin/2016/8326760.pdf",
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size = "12 pages",
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abstract = "In 2012, Moraglio and coauthors introduced new genetic
operators for Genetic Programming, called geometric
semantic genetic operators. They have the very
interesting advantage of inducing a unimodal error
surface for any supervised learning problem. At the
same time, they have the important drawback of
generating very large data models that are usually very
hard to understand and interpret. The objective of this
work is to alleviate this drawback, still maintaining
the advantage. More in particular, we propose an
elitist version of geometric semantic operators, in
which offspring are accepted in the new population only
if they have better fitness than their parents. We
present experimental evidence, on five complex
real-life test problems, that this simple idea allows
us to obtain results of a comparable quality (in terms
of fitness), but with much smaller data models,
compared to the standard geometric semantic operators.
In the final part of the paper, we also explain the
reason why we consider this a significant improvement,
showing that the proposed elitist operators generate
manageable models, while the models generated by the
standard operators are so large in size that they can
be considered unmanageable.",
- }
Genetic Programming entries for
Mauro Castelli
Leonardo Vanneschi
Ales Popovic
Citations