Adapting learning classifier systems to symbolic regression

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

  author =       "Syed S. Naqvi and Will N. Browne",
  booktitle =    "2016 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Adapting learning classifier systems to symbolic
  year =         "2016",
  pages =        "2209--2216",
  abstract =     "Genetic programming (GP) approaches have been widely
                 studied for symbolic regression problems and have
                 achieved substantial progress. This work investigates
                 the effectiveness of niching property and multiple
                 learnt solutions of a Learning Classifier System (LCS)
                 to symbolic regression benchmark problems.
                 Specifically, an XCS with real-valued interval based
                 conditions and code fragmented action termed as XCS-SR
                 is proposed for tackling symbolic regression problem.
                 This is the first LCS ever to address the problem of
                 symbolic regression. The results on nine standard
                 symbolic regression benchmarks show that the proposed
                 XCS-SR method consistently obtains statistically better
                 results on a majority of the benchmarks, in terms of
                 average absolute error together with an increased
                 number of exact solutions as compared with the GP
  keywords =     "genetic algorithms, genetic programming, XCS",
  DOI =          "doi:10.1109/CEC.2016.7744061",
  month =        jul,
  notes =        "PhD thesis Learning
                 Feature Selection and Combination Strategies for
                 Generic Salient Object Detection Naqvi, Syed 2016

                 Also known as \cite{7744061}",

Genetic Programming entries for Syed Saud Naqvi Will N Browne