Evolving Artificial Neural Networks using Cartesian Genetic Programming

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

@PhdThesis{Turner:thesis,
  author =       "Andrew James Turner",
  title =        "Evolving Artificial Neural Networks using Cartesian
                 Genetic Programming",
  school =       "Electronics, University of York",
  year =         "2015",
  address =      "UK",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, ANN",
  URL =          "http://etheses.whiterose.ac.uk/12035/1/thesis.pdf",
  URL =          "http://etheses.whiterose.ac.uk/12035/",
  size =         "336 pages",
  abstract =     "NeuroEvolution is the application of Evolutionary
                 Algorithms to the training of Artificial Neural
                 Networks. NeuroEvolution is thought to possess many
                 benefits over traditional training methods including:
                 the ability to train recurrent network structures, the
                 capability to adapt network topology, being able to
                 create heterogeneous networks of arbitrary transfer
                 functions, and allowing application to reinforcement as
                 well as supervised learning tasks. This thesis presents
                 a series of rigorous empirical investigations into many
                 of these perceived advantages of NeuroEvolution. In
                 this work it is demonstrated that the ability to
                 simultaneously adapt network topology along with
                 connection weights represents a significant advantage
                 of many NeuroEvolutionary methods. It is also
                 demonstrated that the ability to create heterogeneous
                 networks comprising a range of transfer functions
                 represents a further significant advantage. This thesis
                 also investigates many potential benefits and drawbacks
                 of NeuroEvolution which have been largely overlooked in
                 the literature. This includes the presence and role of
                 genetic redundancy in NeuroEvolution's search and
                 whether program bloat is a limitation.

                 The investigations presented focus on the use of a
                 recently developed NeuroEvolution method based on
                 Cartesian Genetic Programming. This thesis extends
                 Cartesian Genetic Programming such that it can
                 represent recurrent program structures allowing for the
                 creation of recurrent Artificial Neural Networks. Using
                 this newly developed extension, Recurrent Cartesian
                 Genetic Programming, and its application to Artificial
                 Neural Networks, are demonstrated to be extremely
                 competitive in the domain of series forecasting",
}

Genetic Programming entries for Andrew James Turner

Citations