Evolution of Robotic Behaviour using Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Jonathan Mwaura",
  title =        "Evolution of Robotic Behaviour using Gene Expression
  school =       "University of Exeter, Department of Computer Science",
  year =         "2011",
  address =      "UK",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  URL =          "http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.549144",
  URL =          "https://ore.exeter.ac.uk/repository/handle/10036/3493",
  URL =          "https://ore.exeter.ac.uk/repository/bitstream/handle/10036/3493/MwauraJ.pdf",
  URL =          "http://hdl.handle.net/10036/3493",
  size =         "191 pages",
  abstract =     "The main objective in automatic robot controller
                 development is to devise mechanisms whereby robot
                 controllers can be developed with less reliance on
                 human developers. One such mechanism is the use of
                 evolutionary algorithms (EAs) to automatically develop
                 robot controllers and occasionally, robot morphology.
                 This area of research is referred to as evolutionary
                 robotics (ER). Through the use of evolutionary
                 techniques such as genetic algorithms (GAs) and genetic
                 programming (GP), ER has shown to be a promising
                 approach through which robust robot controllers can be
                 developed. The standard ER techniques use monolithic
                 evolution to evolve robot behaviour: monolithic
                 evolution involves the use of one chromosome to code
                 for an entire target behaviour. In complex problems,
                 monolithic evolution has been shown to suffer from
                 bootstrap problems; that is, a lack of improvement in
                 fitness due to randomness in the solution set [103,
                 105, 100, 90]. Thus, approaches to dividing the tasks,
                 such that the main behaviours emerge from the
                 interaction of these simple tasks with the robot
                 environment have been devised. These techniques include
                 the subsumption architecture in behaviour based
                 robotics, incremental learning and more recently the
                 layered learning approach [55, 103, 56, 105, 136, 95].
                 These new techniques enable ER to develop complex
                 controllers for autonomous robot. Work presented in
                 this thesis extends the field of evolutionary robotics
                 by introducing Gene Expression Programming (GEP) to the
                 ER field. GEP is a newly developed evolutionary
                 algorithm akin to GA and GP, which has shown great
                 promise in optimisation problems. The presented
                 research shows through experimentation that the unique
                 formulation of GEP genes is sufficient for robot
                 controller representation and development. The obtained
                 results show that GEP is a plausible technique for ER
                 problems. Additionally, it is shown that controllers
                 evolved using GEP algorithm are able to adapt when
                 introduced to new environments. Further, the
                 capabilities of GEP chromosomes to code for more than
                 one gene have been used to show that GEP can be used to
                 evolve manually sub-divided robot behaviours.
                 Additionally, this thesis extends the GEP algorithm by
                 proposing two new evolutionary techniques named
                 multigenic GEP with Linker Evolution (mgGEP-LE) and
                 multigenic GEP with a Regulator Gene (mgGEP-RG). The
                 results obtained from the proposed algorithms show that
                 the new techniques can be used to automatically evolve
                 modularity in robot behaviour. This ability to automate
                 the process of behaviour sub-division and optimisation
                 in a modular chromosome is unique to the GEP
                 formulations discussed, and is an important advance in
                 the development of machines that are able to evolve
                 stratified behavioural architectures with little human
  notes =        "Supervisor Ed Keedwell",

Genetic Programming entries for Jonathan Mwaura