Evolving robot sub-behaviour modules using Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Mwaura:2014:GPEM,
  author =       "Jonathan Mwaura and Ed Keedwell",
  title =        "Evolving robot sub-behaviour modules using Gene
                 Expression Programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "2",
  pages =        "95--131",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, Subsumption architecture,
                 Layered learning, Evolutionary robotics, Robot
                 behaviour coordination",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-014-9229-x",
  size =         "37 pages",
  abstract =     "Many approaches to AI in robotics use a multi-layered
                 approach to determine levels of behaviour from basic
                 operations to goal-directed behaviour, the most
                 well-known of which is the subsumption architecture. In
                 this paper, the performances of the unigenic Gene
                 Expression Programming (ugGEP) and multigenic GEP
                 (mgGEP) in evolving robot controllers for a wall
                 following robot are analysed. Additionally, the paper
                 introduces Regulatory Multigenic Gene Expression
                 Programming, a new evolutionary technique that can be
                 used to automatically evolve modularity in robot
                 behaviour. The proposed technique extends the mgGEP
                 algorithm, by incorporating a regulatory gene as part
                 of the GEP chromosome. The regulatory gene, just as in
                 systems biology, determines which of the genes in the
                 chromosome to express and therefore how the controller
                 solves the problem. In the initial experiments, the
                 proposed algorithm is implemented for a robot wall
                 following problem and the results compared to that of
                 ugGEP and mgGEP. In addition to the wall following
                 behaviour, a robot foraging behaviour is implemented
                 with the aim of investigating whether the position of a
                 specific module (sub-expression tree) in the overall
                 expression tree is of importance when coding for a
                 problem.",
}

Genetic Programming entries for Jonathan Mwaura Ed Keedwell

Citations