Binary Representation in Gene Expression Programming: Towards a Better Scalability

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "Binary Representation in Gene Expression Programming:
                 Towards a Better Scalability",
  author =       "Jose Garcia Moreno-Torres and Xavier Llora and 
                 David E. Goldberg",
  booktitle =    "Ninth International Conference on Intelligent Systems
                 Design and Applications, ISDA '09",
  year =         "2009",
  month =        "30 " # nov # "-2 " # dec,
  pages =        "1441--1444",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, machine learning, classifier
  DOI =          "doi:10.1109/ISDA.2009.33",
  publisher =    "IEEE Computer Society",
  abstract =     "One of the main problems that arises when using gene
                 expression programming (GEP) conditions in learning
                 classifier systems is the increasing number of symbols
                 present as the problem size grows. When doing
                 model-building LCS, this issue limits the scalability
                 of such a technique, due to the cost required. This
                 paper proposes a binary representation of GEP
                 chromosomes to palliate the computation requirements
                 needed. A theoretical reasoning behind the proposed
                 representation is provided, along with empirical
  notes =        "Also known as \cite{5363972}",

Genetic Programming entries for Jose Garcia Moreno-Torres Xavier Llora David E Goldberg