Learning complex, overlapping and niche imbalance Boolean problems using XCS-based classifier systems

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

@Article{Iqbal:2013:EI,
  author =       "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
  title =        "Learning complex, overlapping and niche imbalance
                 {Boolean} problems using {XCS-based} classifier
                 systems",
  journal =      "Evolutionary Intelligence",
  year =         "2013",
  volume =       "6",
  number =       "2",
  pages =        "73--91",
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier systems, XCS, XCSCFA, Code fragments,
                 Overlapping problems, Niche imbalance",
  ISSN =         "1864-5909",
  publisher =    "Springer",
  URL =          "http://dx.doi.org/10.1007/s12065-013-0091-1",
  DOI =          "doi:10.1007/s12065-013-0091-1",
  size =         "19 pages",
  abstract =     "XCS is an accuracy-based learning classifier system,
                 which has been successfully applied to learn various
                 classification and function approximation problems.
                 Recently, it has been reported that XCS cannot learn
                 overlapping and niche imbalance problems using the
                 typical experimental setup. This paper describes two
                 approaches to learn these complex problems: firstly,
                 tune the parameters and adjust the methods of standard
                 XCS specifically for such problems. Secondly, apply an
                 advanced variation of XCS. Specifically, we developed
                 previously an XCS with code-fragment actions, named
                 XCSCFA, which has a more flexible genetic programming
                 like encoding and explicit state-action mapping through
                 computed actions. This approach is examined and
                 compared with standard XCS on six complex Boolean
                 datasets, which include overlapping and niche imbalance
                 problems. The results indicate that to learn
                 overlapping and niche imbalance problems using XCS, it
                 is beneficial to either deactivate action set
                 subsumption or use a relatively high subsumption
                 threshold and a small error threshold. The XCSCFA
                 approach successfully solved the tested complex,
                 overlapping and niche imbalance problems without
                 parameter tuning, because of the rich alphabet,
                 inconsistent actions and especially the redundancy
                 provided by the code-fragment actions. The major
                 contribution of the work presented here is overcoming
                 the identified problem in the wide-spread XCS
                 technique.",
}

Genetic Programming entries for Muhammad Iqbal Will N Browne Mengjie Zhang

Citations