Adaptive Behaviour Based Robotics using On-Board Genetic Programming

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

  title =        "Adaptive Behaviour Based Robotics using On-Board
                 Genetic Programming",
  author =       "Anders Kofod-Petersen",
  school =       "Norwegian University of Science and Technology",
  year =         "2002",
  type =         "Cand. Scient.",
  address =      "Norway",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  URL =          "",
  size =         "121 pages",
  bibsource =    "OAI-PMH server at",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  abstract =     "This thesis investigates the use of Genetic
                 Programming (GP) to evolve controllers for an
                 autonomous robot. GP is a type of Genetic Algorithm
                 (GA) using the Darwinian idea of natural selection and
                 genetic recombination, where the individuals most often
                 is represented as a tree-structure. The GP is used to
                 evolve a population of possible solutions over many
                 generations to solve problems. The most common approach
                 used today, to develop controllers for autonomous
                 robots, is to employ a GA to evolve an Artificial
                 Neural Network (ANN). This approach is most often used
                 in simulation only or in conjunction with online
                 evolution; where simulation still covers the largest
                 part of the process. The GP has been largely neglected
                 in Behaviour Based Robotics (BBR). The is primarily due
                 to the problem of speed, which is the biggest curse of
                 any standard GP. The main contribution of this thesis
                 is the approach of using a linear representation of the
                 GP in online evolution, and to establish whether or not
                 the GP is feasible in this situation. Since this is not
                 a comparison with other methods, only a demonstration
                 of the possibilities with GP, there is no need for
                 testing the particular test cases with other

                 The work in this thesis builds upon the work by
                 Wolfgang Banzhaf and Peter Nordin, and therefor a
                 comparison with their work will be done.",

Genetic Programming entries for Anders Kofod-Petersen