- 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 = "
http://www.idi.ntnu.no/~anderpe/publications/kofod-petersen_MSC2002.pdf",
-
URL = "
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.3070",
- size = "121 pages",
- bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
- contributor = "CiteSeerX",
- language = "en",
- oai = "oai:CiteSeerXPSU:10.1.1.139.3070",
-
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
methods.
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.",
