Tournament Selection based on Statistical Test in Genetic Programming

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

  author =       "Thi Huong Chu and Quang Uy Nguyen and 
                 Michael O'Neill",
  title =        "Tournament Selection based on Statistical Test in
                 Genetic Programming",
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "303--312",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_28",
  abstract =     "Selection plays a critical role in the performance of
                 evolutionary algorithms. Tournament selection is often
                 considered the most popular techniques among several
                 selection methods. Standard tournament selection
                 randomly selects several individuals from the
                 population and the individual with the best fitness
                 value is chosen as the winner. In the context of
                 Genetic Programming, this approach ignores the error
                 value on the fitness cases of the problem emphasising
                 relative fitness quality rather than detailed
                 quantitative comparison. Subsequently, potentially
                 useful information from the error vector may be lost.
                 In this paper, we introduce the use of a statistical
                 test into selection that uses information from the
                 individual's error vector. Two variants of tournament
                 selection are proposed, and tested on Genetic
                 Programming for symbolic regression problems. On the
                 benchmark problems examined we observe a benefit of the
                 proposed methods in reducing code growth and
                 generalisation error.",
  notes =        "PPSN2016",

Genetic Programming entries for Thi Huong Chu Quang Uy Nguyen Michael O'Neill