Evolving optimum populations with XCS classifier systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
  title =        "Evolving optimum populations with XCS classifier
  journal =      "Soft Computing",
  year =         "2013",
  volume =       "17",
  number =       "3",
  pages =        "503--518",
  month =        mar,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier systems, XCS, Optimal populations,
                 Scalability, Code fragments, Action consistency",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-012-0922-5",
  language =     "English",
  size =         "16 pages",
  abstract =     "The main goal of the research xdirection is to extract
                 building blocks of knowledge from a problem domain.
                 Once extracted successfully, these building blocks are
                 to be used in learning more complex problems of the
                 domain, in an effort to produce a scalable learning
                 classifier system (LCS). However, whilst current LCS
                 (and other evolutionary computation techniques)
                 discover good rules, they also create sub-optimum
                 rules. Therefore, it is difficult to separate good
                 building blocks of information from others without
                 extensive post-processing. In order to provide richness
                 in the LCS alphabet, code fragments similar to tree
                 expressions in genetic programming are adopted. The
                 accuracy-based XCS concept is used as it aims to
                 produce maximally general and accurate classifiers,
                 albeit the rule base requires condensation (compaction)
                 to remove spurious classifiers. Serendipitously, this
                 work on scalability of LCS produces compact rule sets
                 that can be easily converted to the optimum population.
                 The main contribution of this work is the ability to
                 clearly separate the optimum rules from others without
                 the need for expensive post-processing for the first
                 time in LCS. This paper identifies that consistency of
                 action in rich alphabets guides LCS to optimum rule
  notes =        "XCS with code fragmented action",

Genetic Programming entries for Muhammad Iqbal Will N Browne Mengjie Zhang