Friendship Modeling for Cooperative Co-Evolutionary Fuzzy Systems: A Hybrid GA-GP Algorithm

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

  author =       "M.-R. Akbarzadeh-T. and I. Mosavat and S. Abbasi",
  title =        "Friendship Modeling for Cooperative Co-Evolutionary
                 Fuzzy Systems: A Hybrid GA-GP Algorithm",
  booktitle =    "Proceedings of the 22nd International Conference of
                 North American Fuzzy Information Processing Society,
                 NAFIPS 2003",
  year =         "2003",
  pages =        "61--66",
  month =        "24-26 " # jul,
  keywords =     "genetic algorithms, genetic programming, Artificial
                 neural networks, Chaos, Computational modelling,
                 Convergence, Evolutionary computation, Fuzzy logic,
                 Fuzzy systems, Genetic programming, Humans, Stochastic
                 processes, cooperative systems, fuzzy systems,
                 groupware, modelling, table lookup, time series,
                 chaotic time series prediction, cooperative
                 co-evolutionary fuzzy systems, friendship modeling,
                 function evaluations, fuzzy lookup tables, hybrid GA-GP
                 algorithm, membership functions, rules sets",
  DOI =          "doi:10.1109/NAFIPS.2003.1226756",
  size =         "6 pages",
  abstract =     "A novel approach is proposed to combine the strengths
                 of GA and GP to optimise rule sets and membership
                 functions of fuzzy systems in a co-evolutionary
                 strategy in order to avoid the problem of dual
                 representation in fuzzy systems. The novelty of
                 proposed algorithm is twofold. One is that GP is used
                 for the structural part (Rule sets) and GA for the
                 string part (Membership functions). The goal is to
                 reduce/eliminate the problem of competing conventions
                 by co-evolving pieces of the problem separately and
                 then in combination. Second is exploiting the synergism
                 between rules sets and membership functions by
                 imitating the effect of 'matching' and friendship in
                 cooperating teams of humans, thereby significantly
                 reducing the number of function evaluations necessary
                 for evolution. The method is applied to a chaotic time
                 series prediction problem and compared with the
                 standard fuzzy table look-up scheme. demonstrate
                 several significant improvements with the proposed
                 approach; specifically, four times higher fitness and
                 more steady fitness improvements as compared with
                 epochal improvements observed in GP.",

Genetic Programming entries for Mohammad-R Akbarzadeh-Totonchi I Mosavat S Abbasi