Modelling Evolvability in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Fowler:2016:EuroGP,
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author = "Benjamin Fowler and Wolfgang Banzhaf",
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title = "Modelling Evolvability in Genetic Programming",
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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 = "215--229",
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organisation = "EvoStar",
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keywords = "genetic algorithms, genetic programming, evolvability,
meta-learning, artificial neural networks",
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isbn13 = "978-3-319-30668-1",
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DOI = "doi:10.1007/978-3-319-30668-1_14",
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abstract = "We develop a tree-based genetic programming system
capable of modelling evolvability during evolution
through machine learning algorithms, and exploiting
those models to increase the efficiency and final
fitness. Existing methods of determining evolvability
require too much computational time to be effective in
any practical sense. By being able to model
evolvability instead, computational time may be
reduced. This will be done first by demonstrating the
effectiveness of modelling these properties \emph{a
priori}, before expanding the system to show its
effectiveness as evolution occurs.",
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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
Benjamin Fowler
Wolfgang Banzhaf
Citations