Demonstrating the Power of Object-Oriented Genetic Programming via the Inference of Graph Models for Complex Networks
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Medland:2014:NaBIC,
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author = "Michael Medland and Kyle Harrison and
Beatrice Ombuki-Berman",
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title = "Demonstrating the Power of Object-Oriented Genetic
Programming via the Inference of Graph Models for
Complex Networks",
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booktitle = "Sixth World Congress on Nature and Biologically
Inspired Computing",
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year = "2014",
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editor = "Ana Maria Madureira and Ajith Abraham and
Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and
Choo yun Huoy",
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pages = "305--311",
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address = "Porto, Portugal",
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month = "30 " # jul # " - 1 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-4799-5937-2/14",
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DOI = "doi:10.1109/NaBIC.2014.6921896",
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abstract = "Traditionally, GP used a single tree-based
representation which does not lend itself well to
state-based programs or multiple behaviours. To
alleviate this drawback, object-oriented GP (OOGP)
introduced a means of evolving programs with multiple
behaviours which could be easily extended to
state-based programs. However, the production of
programs which allowed embedded knowledge and produced
readable code was still not easily addressed using the
OOGP methodology. Exemplified through the evolution of
graph models for complex networks, this paper
demonstrates the benefits of a new approach to OOGP
inspired by abstract classes and linear GP.
Furthermore, the new approach to OOGP, named
LinkableGP, facilitates the embedding of expert
knowledge while also maintaining the benefits of
OOGP.",
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notes = "NaBIC 2014 http://www.mirlabs.net/nabic14/",
- }
Genetic Programming entries for
Michael Medland
Kyle Robert Harrison
Beatrice Ombuki-Berman
Citations