Automatically defined functions for learning classifier systems

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

@InProceedings{Iqbal:2011:GECCOcomp,
  author =       "Muhammad Iqbal and Mengjie Zhang and Will Browne",
  title =        "Automatically defined functions for learning
                 classifier systems",
  booktitle =    "Fourteenth international workshop on learning
                 classifier systems",
  year =         "2011",
  editor =       "Daniele Loiacono and Albert Orriols-Puig and 
                 Ryan Urbanowicz",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming, 20mux",
  pages =        "375--382",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002022",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This work introduces automatically defined functions
                 (ADFs) for learning classifier systems (LCS). ADFs had
                 been successfully implemented in genetic programming
                 (GP)for various domain problems such as multiplexer and
                 even-odd parity, but they have never been attempted in
                 LCS research field before. ADFs in GP contract program
                 trees and shorten training times whilst providing
                 resilience to destructive genetic operators. We have
                 implemented ADFs in Wilson's accuracy based LCS, known
                 as XCS [14]. This initial investigation of ADFs in LCS
                 shows that the multiple genotypes to a phenotype issue
                 in feature rich encodings disables the subsumption
                 deletion function. The additional methods and increased
                 search space also leads to much longer training times.
                 This is compensated by the ADFs containing useful
                 knowledge, such as the importance of the address bits
                 in the multiplexer problem. The ADFs also create masks
                 that autonomously subdivide the search space into areas
                 of interest and uniquely, areas of not interest. The
                 next stage of this work is to implement simplification
                 methods and then determine methods by which ADFs can
                 facilitate scaling for more complex problems within the
                 same problem domain.",
  notes =        "Also known as \cite{2002022} Distributed on CD-ROM at
                 GECCO-2011.

                 ACM Order Number 910112.",
}

Genetic Programming entries for Muhammad Iqbal Mengjie Zhang Will N Browne

Citations