Breaking the Stereotypical Dogma of Artificial Neural Networks with Cartesian Genetic Programming

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

  author =       "Gul Muhammad Khan and Arbab Masood Ahmad",
  title =        "Breaking the Stereotypical Dogma of Artificial Neural
                 Networks with Cartesian Genetic Programming",
  booktitle =    "Inspired by Nature: Essays Presented to Julian F.
                 Miller on the Occasion of his 60th Birthday",
  publisher =    "Springer",
  year =         "2017",
  editor =       "Susan Stepney and Andrew Adamatzky",
  volume =       "28",
  series =       "Emergence, Complexity and Computation",
  chapter =      "10",
  pages =        "213--233",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, ANN",
  isbn13 =       "978-3-319-67996-9",
  DOI =          "doi:10.1007/978-3-319-67997-6_10",
  abstract =     "This chapter presents the work done in the field of
                 Cartesian Genetic Programming evolved Artificial Neural
                 Networks (CGPANN). Three types of CGPANN are presented,
                 the Feed-forward CGPANN (FFCGPAN), Recurrent CGPANN and
                 the CGPANN that has developmental plasticity, also
                 called Plastic CGPANN or PCGPANN. Each of these
                 networks is explained with the help of diagrams.
                 Performance results obtained for a number of benchmark
                 problems using these networks are illustrated with the
                 help of tables. Artificial Neural Networks (ANNs)
                 suffer from the dilemma of how to select complexity of
                 the network for a specific task, what should be the
                 pattern of inter-connectivity, and in case of feedback,
                 what topology will produce the best possible results.
                 Cartesian Genetic Programming (CGP) offers the ability
                 to select not only the desired network complexity but
                 also the inter-connectivity patterns, topology of
                 feedback systems, and above all, decides which input
                 parameters should be weighted more or less and which
                 one to be neglected. In this chapter we discuss how CGP
                 is used to evolve the architecture of Neural Networks
                 for optimum network and characteristics. Don't you want
                 a system that designs everything for you? That helps
                 you select the optimal network, the inter-connectivity,
                 the topology, the complexity, input parameters
                 selection and input sensitivity? If yes, then CGP
                 evolved Artificial Neural Network (CGPANN) and CGP
                 evolved Recurrent Neural Network (CGPRNN) is the answer
                 to your questions.",
  notes =        "part of \cite{miller60book}

Genetic Programming entries for Gul Muhammad Khan Arbab Masood Ahmad