Condition Matrix Based Genetic Programming for Rule Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  title =        "Condition Matrix Based Genetic Programming for Rule
  author =       "Jin Feng Wang and Kin-Hong Lee and Kwong-Sak Leung",
  year =         "2006",
  booktitle =    "18th IEEE International Conference on Tools with
                 Artificial Intelligence (ICTAI'06)",
  pages =        "315--322",
  address =      "Arlington, VA, USA",
  month =        nov # " 13-15",
  publisher =    "IEEE Computer Society",
  bibdate =      "2007-01-04",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-2728-0",
  DOI =          "doi:10.1109/ICTAI.2006.45",
  abstract =     "Most genetic programming paradigms are
                 population-based and require huge amount of memory. In
                 this paper, we review the Instruction Matrix based
                 Genetic Programming which maintains all program
                 components in a instruction matrix (IM) instead of
                 manipulating a population of programs. A genetic
                 program is extracted from the matrix just before it is
                 being evaluated. After each evaluation, the fitness of
                 the genetic program is propagated to its corresponding
                 cells in the matrix. Then, we extend the instruction
                 matrix to the condition matrix (CM) for generating rule
                 base from datasets. CM keeps some of characteristics of
                 IM and incorporates the information about rule
                 learning. In the evolving process, we adopt an elitist
                 idea to keep the better rules alive to the end. We
                 consider that genetic selection maybe lead to the huge
                 size of rule set, so the reduct theory borrowed from
                 Rough Sets is used to cut the volume of rules and keep
                 the same fitness as the original rule set. In
                 experiments, we compare the performance of Condition
                 Matrix for Rule Learning (CMRL) with other traditional
                 algorithms. Results are presented in detail and the
                 competitive advantage and drawbacks of CMRL are
  notes =        "",

Genetic Programming entries for Phoenix Jinfeng Wang Kin-Hong Lee Kwong-Sak Leung