Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Muhammad Iqbal and Will N. Browne and Mengjie Zhang",
  title =        "Reusing Building Blocks of Extracted Knowledge to
                 Solve Complex, Large-Scale Boolean Problems",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2014",
  volume =       "18",
  number =       "4",
  pages =        "465--480",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, XCS, Learning
                 Classifier Systems, Layered Learning, Scalability,
                 Building Blocks, Code Fragments",
  ISSN =         "1089-778X",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1109/TEVC.2013.2281537",
  size =         "16 pages",
  abstract =     "Evolutionary computation techniques have had limited
                 capabilities in solving large-scale problems due to the
                 large search space demanding large memory and much
                 longer training times. In the work presented here, a
                 genetic programming like rich encoding scheme has been
                 constructed to identify building blocks of knowledge in
                 a learning classifier system. The fitter building
                 blocks from the learning system trained against smaller
                 problems have been used in a higher complexity problem
                 in the domain in order to achieve scalable learning.
                 The proposed system has been examined and evaluated on
                 four different Boolean problem domains, i.e.
                 multiplexer, majority-on, carry, and even-parity
                 problems. The major contribution of this work is to
                 successfully extract useful building blocks from
                 smaller problems and reuse them to learn more complex,
                 large-scale problems in the domain, e.g. 135-bits
                 multiplexer problem, where the number of possible
                 instances is 2**135 = 4.0 10**40, is solved by reusing
                 the extracted knowledge from the learnt lower level
                 solutions in the domain. Autonomous scaling is, for the
                 first time, shown to be possible in learning classifier
                 systems. It improves effectiveness and reduces the
                 number of training instances required in large
                 problems, but requires more time due to its sequential
                 build-up of knowledge.",
  notes =        "Entered for 2013 HUMIES GECCO 2013


Genetic Programming entries for Muhammad Iqbal Will N Browne Mengjie Zhang