An initialization technique for geometric semantic GP based on demes evolution and despeciation

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

@InProceedings{vanneschi:2017:CEC,
  author =       "Leonardo Vanneschi and Illya Bakurov and 
                 Mauro Castelli",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "An initialization technique for geometric semantic GP
                 based on demes evolution and despeciation",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "113--120",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Initializing the population is a crucial step for
                 genetic programming, and several strategies have been
                 proposed so far. The issue is particularly important
                 for geometric semantic genetic programming, where
                 initialization is known to play a very important role.
                 In this paper, we propose an initialization technique
                 inspired by the biological phenomenon of demes
                 despeciation, i.e. the combination of demes of
                 previously distinct species into a new population. In
                 synthesis, the initial population of geometric semantic
                 genetic programming is created using the best
                 individuals of a set of separate subpopulations, or
                 demes, some of which run standard genetic programming
                 and the others geometric semantic genetic programming
                 for few generations. Geometric semantic genetic
                 programming with this novel initialization technique is
                 shown to outperform geometric semantic genetic
                 programming using the traditional ramped half-and-half
                 algorithm on six complex symbolic regression
                 applications. More specifically, on the studied
                 problems, the proposed initialization technique allows
                 us to generate solutions with comparable or even better
                 generalization ability, and of significantly smaller
                 size than the ramped half-and-half algorithm.",
  keywords =     "genetic algorithms, genetic programming, regression
                 analysis, biological phenomenon, complex symbolic
                 regression applications, demes despeciation, demes
                 evolution, geometric semantic GP, initialization
                 technique, Evolution (biology), Semantics, Sociology,
                 Standards, Statistics",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969303",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969303}",
}

Genetic Programming entries for Leonardo Vanneschi Illya Bakurov Mauro Castelli

Citations