Improving Modularity in Genetic Programming Using Graph-Based Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Istvan Jonyer and Akiko Himes",
  title =        "Improving Modularity in Genetic Programming Using
                 Graph-Based Data Mining",
  booktitle =    "Proceedings of the Nineteenth International Florida
                 Artificial Intelligence Research Society Conference",
  year =         "2006",
  editor =       "Geoff C. J. Sutcliffe and Randy G. Goebel",
  pages =        "556--561",
  address =      "Melbourne Beach, Florida, USA",
  month =        may # " 11-13",
  publisher =    "American Association for Artificial Intelligence",
  keywords =     "genetic algorithms, genetic programming, Machine
                 Learning and Discovery",
  URL =          "",
  abstract =     "We propose to improve the efficiency of genetic
                 programming, a method to automatically evolve computer
                 programs. We use graph-based data mining to identify
                 common aspects of highly fit individuals and
                 modularising them by creating functions out of the
                 subprograms identified. Empirical evaluation on the
                 lawn mower problem shows that our approach is
                 successful in reducing the number of generations needed
                 to find target programs. Even though the graph-based
                 data mining system requires additional processing time,
                 the number of individuals required in a generation can
                 also be greatly reduced, resulting in an overall
  notes =        "cited by \cite{Spector:2011:GECCO}


Genetic Programming entries for Istvan Jonyer Akiko Himes