GP-Based Kernel Evolution for L2-Regularization Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  title =        "{GP}-Based Kernel Evolution for {L2}-Regularization
  author =       "Simone Scardapane and Danilo Comminiello and 
                 Michele Scarpiniti and Aurelio Uncini",
  pages =        "1674--1681",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier systems, Classification, clustering and data
  DOI =          "doi:10.1109/CEC.2014.6900389",
  abstract =     "In kernel-based learning methods, a crucial design
                 parameter is given by the choice of the kernel function
                 to be used. Although there is, in theory, an infinite
                 range of potential candidates, a handful of kernels
                 covers the majority of actual applications. Partly,
                 this is due to the difficulty of choosing an optimal
                 kernel function in absence of a-priori information. In
                 this respect, Genetic Programming (GP) techniques have
                 shown interesting capabilities of learning non-trivial
                 kernel functions that outperform commonly used ones.
                 However, experiments have been restricted to the use of
                 Support Vector Machines (SVMs), and have not addressed
                 some problems that are specific to GP implementations,
                 such as diversity maintenance. In these respects, the
                 aim of this paper is twofold. First, we present a
                 customised GP-based kernel search method that we apply
                 using an L2-Regularisation Network as the base learning
                 algorithm. Second, we investigate the problem of
                 diversity maintenance in the context of kernel
                 evolution, and test an adaptive criterion for
                 maintaining it in our algorithm. For the former point,
                 experiments show a gain in accuracy for our method
                 against fine-tuned standard kernels. For the latter, we
                 show that diversity is decreasing critically fast
                 during the GP iterations, but this decrease does not
                 seems to affect performance of the algorithm.",
  notes =        "WCCI2014",

Genetic Programming entries for Simone Scardapane Danilo Comminiello Michele Scarpiniti Aurelio Uncini