A New Wave: A Dynamic Approach to Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "David Medernach and Jeannie Fitzgerald and 
                 R. Muhammad Atif Azad and Conor Ryan",
  title =        "A New Wave: A Dynamic Approach to Genetic
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "757--764",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908857",
  abstract =     "Wave is a novel form of semantic genetic programming
                 which operates by optimising the residual errors of a
                 succession of short genetic programming runs, and then
                 producing a cumulative solution. These short genetic
                 programming runs are called periods, and they have
                 heterogeneous parameters. In this paper we leverage the
                 potential of Wave's heterogeneity to simulate a dynamic
                 evolutionary environment by incorporating self adaptive
                 parameters together with an innovative approach to
                 population renewal. We conduct an empirical study
                 comparing this new approach with multiple linear
                 regression (MLR) as well as several evolutionary
                 computation (EC) methods including the well known
                 geometric semantic genetic programming (GSGP) together
                 with several other optimised Wave techniques. The
                 results of our investigation show that the dynamic Wave
                 algorithm delivers consistently equal or better
                 performance than Standard GP (both with or without
                 linear scaling), achieves testing fitness equal or
                 better than multiple linear regression, and performs
                 significantly better than GSGP on five of the six
                 problems studied.",
  notes =        "BDS Group CSIS Department University of Limerick

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",

Genetic Programming entries for David Medernach Jeannie Fitzgerald R Muhammad Atif Azad Conor Ryan