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:1.8620

@InProceedings{Medland:2014:NaBIC,

• author = "Michael Medland and Kyle Harrison and Beatrice Ombuki-Berman",
• title = "Demonstrating the Power of Object-Oriented Genetic Programming via the Inference of Graph Models for Complex Networks",
• booktitle = "Sixth World Congress on Nature and Biologically Inspired Computing",
• year = "2014",
• editor = "Ana Maria Madureira and Ajith Abraham and Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and Choo yun Huoy",
• pages = "305--311",
• address = "Porto, Portugal",
• month = "30 " # jul # " - 1 " # jul,
• publisher = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4799-5937-2/14",
• DOI = "10.1109/NaBIC.2014.6921896",
• 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.",
• notes = "NaBIC 2014 http://www.mirlabs.net/nabic14/",

}

Genetic Programming entries for Michael Medland Kyle Robert Harrison Beatrice Ombuki-Berman

Citations