XCS with stack-based genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Pier Luca Lanzi",
  title =        "{XCS} with stack-based genetic programming",
  booktitle =    "Proceedings of the 2003 Congress on Evolutionary
                 Computation CEC2003",
  editor =       "Ruhul Sarker and Robert Reynolds and 
                 Hussein Abbass and Kay Chen Tan and Bob McKay and Daryl Essam and 
                 Tom Gedeon",
  pages =        "1186--1191",
  year =         "2003",
  publisher =    "IEEE Press",
  address =      "Canberra",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "8-12 " # dec,
  organisation = "IEEE Neural Network Council (NNC), Engineers Australia
                 (IEAust), Evolutionary Programming Society (EPS),
                 Institution of Electrical Engineers (IEE)",
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, Computer hacking, Genetic mutations,
                 Intelligent robots, Laboratories, Learning automata,
                 Proposals, System testing, data structures, knowledge
                 based systems, learning (artificial intelligence),
                 learning systems, linear programming, pattern
                 classification, classifier condition representation,
                 learning classifier system, linear programming, mutate
                 classifier, reverse polish notation expression,
                 stack-based genetic programming, virtual stack
  ISBN =         "0-7803-7804-0",
  URL =          "http://webspace.elet.polimi.it/lanzi/papers//lanzi2003cecstack.pdf",
  DOI =          "doi:10.1109/CEC.2003.1299803",
  abstract =     "We present an extension of the learning classifier
                 system XCS in which classifier conditions are
                 represented by RPN expressions and stack-based Genetic
                 Programming is used to recombine and mutate
                 classifiers. In contrast with other extensions of XCS
                 involving tree-based Genetic Programming, the
                 representation we apply here produces conditions that
                 are linear programs, interpreted by a virtual stack
                 machine (similar to a pushdown automaton), and
                 recombined through standard genetic operators. We test
                 the version of XCS extended with stack-based conditions
                 on a set of problems of different complexity.",
  notes =        "CEC 2003 - A joint meeting of the IEEE, the IEAust,
                 the EPS, and the IEE.",

Genetic Programming entries for Pier Luca Lanzi