Evolving Neural Networks for Multi-robot Odor Search

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Joao Macedo and Lino Marques and Ernesto Costa",
  booktitle =    "2016 International Conference on Autonomous Robot
                 Systems and Competitions (ICARSC)",
  title =        "Evolving Neural Networks for Multi-robot Odor Search",
  year =         "2016",
  pages =        "288--293",
  abstract =     "The tasks of odour detection, plume tracking and odour
                 source localization constitute an important, yet
                 complex, real world problem. One possible solution for
                 them is based on the use of a group of mobile robots
                 whose controllers have to be defined. Artificial Neural
                 Networks (ANN) have already been used as controllers,
                 but the task of hand defining their topology and
                 parameters can be very challenging and time consuming.
                 In this paper, we propose an approach to evolve, rather
                 than design, ANN-based controllers. Our approach relies
                 on Genetic Programming (GP), a family of stochastic
                 search procedures loosely inspired by the biological
                 principles of Natural Selection and Genetics. We
                 compare our approach with a classic one, inspired by
                 the chemotaxis behaviour of the E. coli bacteria. Our
                 results show that this approach is able to outperform
                 the chemotaxis in the experiments performed.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICARSC.2016.37",
  month =        may,
  notes =        "Also known as \cite{7781991}",

Genetic Programming entries for Joao Macedo Lino Marques Ernesto Costa