Rule-based Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{WZG2007DGPFi,
  author =       "Thomas Weise and Michael Zapf and Kurt Geihs",
  title =        "Rule-based Genetic Programming",
  booktitle =    "Proceedings of BIONETICS 2007, 2nd International
                 Conference on Bio-Inspired Models of Network,
                 Information, and Computing Systems",
  publisher =    "Institute for Computer Sciences, Social-Informatics
                 and Telecommunications Engineering (ICST), IEEE, ACM",
  year =         "2007",
  pages =        "8--15",
  month =        "10-12 " # dec,
  affiliation =  "University of Kassel",
  address =      "Radisson SAS Beke Hotel, 43. Terez krt., Budapest
                 H-1067, Hungary",
  keywords =     "genetic algorithms, genetic programming, Rule-based
                 Genetic Programming, GP, Distributed Systems, Critical
                 Section, Epistasis, Neutrality, Learning Classifier
                 Systems",
  isbn13 =       "978-963-9799-05-9",
  language =     "en",
  URL =          "http://www.it-weise.de/documents/files/WZG2007RBGP.pdf",
  DOI =          "doi:10.1109/BIMNICS.2007.4610073",
  abstract =     "In this paper we introduce a new approach for Genetic
                 Programming, called rule-based Genetic Programming, or
                 RBGP in short. A program evolved in the RBGP syntax is
                 a list of rules. Each rule consists of two conditions,
                 combined with a logical operator, and an action part.
                 Such rules are independent from each other in terms of
                 position (mostly) and cardinality (always). This
                 reduces the epistasis drastically and hence, the
                 genetic reproduction operations are much more likely to
                 produce good results than in other Genetic Programming
                 methodologies. we apply RBGP to a hard problem in
                 distributed systems. With it, we are able to obtain
                 emergent algorithms for mutual exclusion at a
                 distributed critical section.",
  notes =        "Also known as \cite{4610073}",
}

Genetic Programming entries for Thomas Weise Michael Zapf Kurt Geihs

Citations