Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks

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-bas...

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Vydané v:2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) s. 305 - 311
Hlavní autori: Medland, Michael Richard, Harrison, Kyle Robert, Ombuki-Berman, Beatrice M.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.07.2014
Predmet:
biologically inspired algorithms
complex networks
Computational modeling
evolutionary computation
genetic programming
Genetics
object-orientation
Programming
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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.
AbstractList 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.
Author Medland, Michael Richard
Harrison, Kyle Robert
Ombuki-Berman, Beatrice M.
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  givenname: Kyle Robert
  surname: Harrison
  fullname: Harrison, Kyle Robert
  email: kh08uh@brocku.ca
  organization: Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
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  givenname: Beatrice M.
  surname: Ombuki-Berman
  fullname: Ombuki-Berman, Beatrice M.
  email: bombuki@brocku.ca
  organization: Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
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Snippet Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this...
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StartPage 305
SubjectTerms biologically inspired algorithms
complex networks
Computational modeling
evolutionary computation
genetic programming
Genetics
object-orientation
Programming
Title Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks
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