Multi-Objective Genetic Programming Based Design of Fuzzy Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "M. Freischlad and M. Schnellenbach-Held",
  title =        "Multi-Objective Genetic Programming Based Design of
                 Fuzzy Systems",
  booktitle =    "Proceedings of the 2005 ASCE International Conference
                 on Computing in Civil Engineering",
  year =         "2005",
  editor =       "Lucio Soibelman and Feniosky Pena-Mora",
  address =      "Cancun, Mexico",
  month =        jul # " 12-15",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1061/40794(179)62",
  abstract =     "The Multi-Objective Domain Knowledge Augmented Genetic
                 Fuzzy System (MODA-GFS) is a GP based fuzzy system for
                 the data-driven generation of fuzzy rule based systems.
                 The algorithm incorporates domain specific knowledge
                 that is used by human knowledge engineers in the manual
                 fuzzy system design process. The combination of
                 characteristics of two individuals is most interesting
                 if both individuals complement each other. In terms of
                 fuzzy systems this means a potential crossover partner
                 (parent B) has a lower approximation error in an area
                 of the input space, where parent A has a higher error.
                 Within MODA-GFS a method for the determination of
                 feasible crossover mates is implemented. In addition
                 MODA-GFS includes a method for the goal-oriented
                 selection of parent rules that are handed down to the
                 offspring. Especially in the domain of knowledge
                 representation the quality of a fuzzy system is not
                 only determined by its approximation capability but
                 also by its transparency. In order to assure the
                 automated generation of fuzzy systems that are both
                 accurate and transparent multi-objective optimisation
                 methods are implemented. Tests carried out on test
                 functions as well as on real world data sets have shown
                 that the incorporation of domain knowledge
                 significantly speeds up the evolution process. Besides
                 these test results the integration and application of
                 the new methods for automated generation of fuzzy
                 models within a learning expert system environment are
                 described in this paper. Finally an outlook on the
                 current and future work is given, i.e. the transfer of
                 the presented findings to the evolutionary optimisation
                 of large-scale structures.",
  notes =        "c2005 ASCE",

Genetic Programming entries for Mark Freischlad Martina Schnellenbach-Held