Strategies for Improving the Distribution of Random Function Outputs in GSGP
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- @InProceedings{Oliveira:2017:EuroGP,
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author = "Luiz Otavio V. B. Oliveira and Felipe Casadei and
Gisele Pappa",
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title = "Strategies for Improving the Distribution of Random
Function Outputs in GSGP",
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booktitle = "EuroGP 2017: Proceedings of the 20th European
Conference on Genetic Programming",
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year = "2017",
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month = "19-21 " # apr,
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editor = "Mauro Castelli and James McDermott and
Lukas Sekanina",
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series = "LNCS",
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volume = "10196",
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publisher = "Springer Verlag",
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address = "Amsterdam",
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pages = "164--177",
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organisation = "species",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-319-55695-6",
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DOI = "doi:10.1007/978-3-319-55696-3_11",
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abstract = "In the last years, different approaches have been
proposed to introduce semantic information to genetic
programming. In particular, the geometric semantic
genetic programming (GSGP) and the interesting
properties of its evolutionary operators have gotten
the attention of the community. This paper is
interested in the use of GSGP to solve symbolic
regression problems, where semantics is defined by the
output set generated by a given individual when applied
to the training cases. In this scenario, both mutation
and crossover operators defined with fitness function
based on Manhattan distance use randomly built
functions to generate offspring. However, the outputs
of these random functions are not guaranteed to be
uniformly distributed in the semantic space, as the
functions are generated considering the syntactic
space. We hypothesize that the non-uniformity of the
semantics of these functions may bias the search, and
propose three different standard normalization
techniques to improve the distribution of the outputs
of these random functions over the semantic space. The
results are compared with a popular strategy that uses
a logistic function as a wrapper to the outputs, and
show that the strategies tested can improve the results
of the previous method. The experimental analysis also
indicates that a more uniform distribution of the
semantics of these functions does not necessarily imply
in better results in terms of test error.",
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notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
Luiz Otavio Vilas Boas Oliveira
Felipe Casadei
Gisele L Pappa
Citations