Plastic Fitness Predictors Coevolved with Cartesian Programs
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Wiglasz:2016:EuroGP,
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author = "Michal Wiglasz and Michaela Drahosova",
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title = "Plastic Fitness Predictors Coevolved with Cartesian
Programs",
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booktitle = "EuroGP 2016: Proceedings of the 19th European
Conference on Genetic Programming",
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year = "2016",
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month = "30 " # mar # "--1 " # apr,
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editor = "Malcolm I. Heywood and James McDermott and
Mauro Castelli and Ernesto Costa and Kevin Sim",
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series = "LNCS",
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volume = "9594",
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publisher = "Springer Verlag",
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address = "Porto, Portugal",
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pages = "164--179",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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isbn13 = "978-3-319-30668-1",
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DOI = "doi:10.1007/978-3-319-30668-1_11",
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abstract = "Coevolution of fitness predictors, which are a small
sample of all training data for a particular task, was
successfully used to reduce the computational cost of
the design performed by cartesian genetic programming.
However, it is necessary to specify the most
advantageous number of fitness cases in predictors,
which differs from task to task. This paper introduces
a new type of directly encoded fitness predictors
inspired by the principles of phenotypic plasticity.
The size of the coevolved fitness predictor is adapted
in response to the learning phase that the program
evolution goes through. It is shown in 5 symbolic
regression tasks that the proposed algorithm is able to
adapt the number of fitness cases in predictors in
response to the solved task and the program evolution
flow.",
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notes = "Part of \cite{Heywood:2016:GP} EuroGP'2016 held in
conjunction with EvoCOP2016, EvoMusArt2016 and
EvoApplications2016",
- }
Genetic Programming entries for
Michal Wiglasz
Michaela Sikulova
Citations