Improving genetic search in XCS-based classifier systems through understanding the evolvability of classifier rules

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@Article{Iqbal:2015:SC,
  author =       "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
  title =        "Improving genetic search in XCS-based classifier
                 systems through understanding the evolvability of
                 classifier rules",
  journal =      "Soft Computing",
  year =         "2015",
  volume =       "19",
  number =       "7",
  pages =        "1863--1880",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier systems, XCS, XCSCFA, Evolvability",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-014-1369-7",
  size =         "18 pages",
  abstract =     "Learning classifier systems (LCSs), an established
                 evolutionary computation technique, are over 30 years
                 old with much empirical testing and foundations of
                 theoretical understanding. XCS is a well-tested LCS
                 model that generates optimal (i.e., maximally general
                 and accurate) classifier rules in the final solution.
                 Previous work has hypothesised the evolution mechanisms
                 in XCS by identifying the bounds of learning and
                 population requirements. However, no work has shown
                 exactly how an optimum rule is evolved or especially
                 identifies whether the methods within an LCS are being
                 effectively. In this paper, we introduce a method to
                 trace the evolution of classifier rules generated in an
                 XCS-based classifier system. Specifically, we introduce
                 the concept of a family tree, termed parent-tree, for
                 each individual classifier rule generated in the system
                 during training, which describes the whole generational
                 process for that classifier. Experiments are conducted
                 on two sample Boolean problem domains, i.e.,
                 multiplexer and count ones problems using two XCS-based
                 systems, i.e., standard XCS and XCS with code-fragment
                 actions. The analysis of parent-trees reveals, for the
                 first time in XCS, that no matter how specific or
                 general the initial classifiers are, all the optimal
                 classifiers are converged through the mechanism be
                 specific then generalize near the final stages of
                 evolution. Populations where the initial classifiers
                 were slightly more specific than the known ideal
                 specificity in the target solutions evolve faster than
                 either very specific, ideal or more general starting
                 classifier populations. Consequently introducing the
                 flip mutation method and reverting the conventional
                 wisdom back to apply rule discovery in the match set
                 has demonstrated benefits in binary classification
                 problems, which has implications in using XCS for
                 knowledge discovery tasks. It is further concluded that
                 XCS does not directly all relevant information or all
                 breeding strategies to evolve the optimum solution,
                 indicating areas for performance and efficiency
                 improvement in XCS-based systems.",
}

Genetic Programming entries for Muhammad Iqbal Will N Browne Mengjie Zhang

Citations