Fast learning neural networks using Cartesian genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@Article{Khan:2013:Neurocomputing,
  author =       "Maryam Mahsal Khan and Arbab Masood Ahmad and 
                 Gul Muhammad Khan and Julian F. Miller",
  title =        "Fast learning neural networks using Cartesian genetic
                 programming",
  journal =      "Neurocomputing",
  year =         "2013",
  volume =       "121",
  pages =        "274--289",
  month =        "9 " # dec,
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming, Artificial neural network, Pole
                 balancing, Breast cancer, Neuroevolution, Recurrent
                 networks",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2013.04.005",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231213004499",
  URL =          "http://results.ref.ac.uk/Submissions/Output/3354639",
  abstract =     "A fast learning neuroevolutionary algorithm for both
                 feedforward and recurrent networks is proposed. The
                 method is inspired by the well known and highly
                 effective Cartesian genetic programming (CGP)
                 technique. The proposed method is called the CGP-based
                 Artificial Neural Network (CGPANN). The basic idea is
                 to replace each computational node in CGP with an
                 artificial neuron, thus producing an artificial neural
                 network. The capabilities of CGPANN are tested in two
                 diverse problem domains. Firstly, it has been tested on
                 a standard benchmark control problem: single and double
                 pole for both Markovian and non-Markovian cases.
                 Results demonstrate that the method can generate
                 effective neural architectures in substantially fewer
                 evaluations in comparison to previously published
                 neuroevolutionary techniques. In addition, the evolved
                 networks show improved generalisation and robustness in
                 comparison with other techniques. Secondly, we have
                 explored the capabilities of CGPANNs for the diagnosis
                 of Breast Cancer from the FNA (Finite Needle
                 Aspiration) data samples. The results demonstrate that
                 the proposed algorithm gives 99.5percent accurate
                 results, thus making it an excellent choice for pattern
                 recognitions in medical diagnosis, owing to its
                 properties of fast learning and accuracy. The power of
                 a CGP based ANN is its representation which leads to an
                 efficient evolutionary search of suitable topologies.
                 This opens new avenues for applying the proposed
                 technique to other linear/non-linear and
                 Markovian/non-Markovian control and pattern recognition
                 problems.",
  uk_research_excellence_2014 = "This work advances the evolution of
                 artificial neural networks. In collaboration with
                 University of Engineering Technology in Peshawar,
                 Pakistan, the work is the first journal publication on
                 the evolution of artificial neural networks using
                 Cartesian Genetic programming. Development of this work
                 by a PhD student won best student paper at
                 International Conference on Artificial Intelligence,
                 Cambridge, 2013.",
}

Genetic Programming entries for Maryam Mahsal Khan Arbab Masood Ahmad Gul Muhammad Khan Julian F Miller

Citations