A novel NeuroEvolutionary algorithm: Cartesian genetic programming evolved artificial neural network (CGPANN)

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

@InProceedings{Khan:2010:FIT,
  author =       "Maryam Mahsal Khan and Gul Muhammad Khan",
  title =        "A novel NeuroEvolutionary algorithm: Cartesian genetic
                 programming evolved artificial neural network
                 ({CGPANN})",
  booktitle =    "Proceedings of the 8th International Conference on
                 Frontiers of Information Technology",
  year =         "2010",
  pages =        "48:1--48:4",
  articleno =    "48",
  address =      "Islamabad, Pakistan",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, ANN, neuroevolution, inverted
                 pendulum, pole balancing",
  isbn13 =       "978-1-4503-0342-2",
  DOI =          "doi:10.1145/1943628.1943676",
  size =         "4 pages",
  abstract =     "Cartesian Genetic Programming based Neuroevolutionary
                 algorithm is proposed. It encodes the neural network
                 attributes namely weights, topology and functions and
                 then evolves them for best possible weight, topology
                 and function. The architecture generated are both
                 feedforward and recurrent. The proposed algorithm is
                 applied on the standard benchmark control problem:
                 balancing single and double pole at both markovian and
                 non-markovian states. Results demonstrate that CGPANN
                 has the potential to generate neural architecture and
                 parameters in substantially fewer number of evaluations
                 in comparison to earlier neuroevolutionary techniques.
                 The power of CGPANN is its representation which leads
                 to a thorough evolutionary search producing generalized
                 networks. This opens new avenues of applying the
                 proposed technique to any non-linear and dynamic
                 problem.",
  acmid =        "1943676",
  notes =        "University of Engineering & Technology, Peshawar,
                 Pakistan",
}

Genetic Programming entries for Maryam Mahsal Khan Gul Muhammad Khan

Citations