Genetic Programming Design of Fuzzy Controllers for Mobile Robot Path Tracking

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

  author =       "Abdollah Homaifar and D. Battle and E. Tunstel and 
                 G. Dozier",
  title =        "Genetic Programming Design of Fuzzy Controllers for
                 Mobile Robot Path Tracking",
  journal =      "International Journal of Knowledge-Based Intelligent
                 Engineering Systems",
  year =         "2000",
  volume =       "4",
  number =       "1",
  pages =        "33--52",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Genetic programming (GP) is an evolutionary strategy
                 that attempts to deal with the notion of how computers
                 can learn to solve problems without being explicitly
                 programmed. It has been demonstrated that GP, under the
                 influence of Darwinian concepts, could genetically
                 breed computer programs to approximately solve problems
                 in a variety of applications. One primary example is
                 its application to the problem of automatically
                 learning nonlinear mappings that govern the behavior of
                 control systems. It is demonstrated here that GP can
                 formulate such nonlinear maps in the form of fuzzy
                 control rules, which yield comparable or better
                 performance than one derived through manual design
                 using trial-and-error. The objective is to address the
                 efficient implementation of GP for the discovery of
                 knowledge bases intended for use in fuzzy logic
                 controller applications. Efficiency is achieved with a
                 C programming language implementation of GP, which is
                 applied to a mobile robot steering control problem.
                 Robot path following performance is compared to results
                 obtained using an existing GP implementation in the
                 LISP programming language. It is demonstrated that the
                 C implementation has a definite advantage with regard
                 to computational speed of evolution. In this work, we
                 have extended the application of GP to handle
                 simultaneous evolution of membership functions and rule
                 bases for the same control problem. Furthermore, GP is
                 used to handle selection of fuzzy t-norms. It is
                 concluded that simultaneous evolution of rule bases and
                 membership functions with t-norm selection results in
                 enhanced performance of the evolved controllers.
                 Finally, the robustness characteristics of the
                 genetically evolved fuzzy controllers are investigated
                 by examining the effects of sensor measurement noise
                 and an increase in the robot's nominal forward
  notes =        "Nov 2012 IJKBIES web site not listing stuff before

Genetic Programming entries for Abdollah Homaifar Daryl Battle Edward W Tunstel Gerry Dozier