Semantic tournament selection for genetic programming based on statistical analysis of error vectors

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Thi Houng Chu and Quang Uy Nguyen and 
                 Michael O'Neill",
  title =        "Semantic tournament selection for genetic programming
                 based on statistical analysis of error vectors",
  journal =      "Information Sciences",
  year =         "2018",
  volume =       "436-437",
  pages =        "352--366",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming Tournament
                 selection, Statistical test, Code bloat, Semantics",
  DOI =          "doi:10.1016/j.ins.2018.01.030",
  size =         "15 pages",
  abstract =     "The selection mechanism plays a very important role in
                 the performance of Genetic Programming (GP). Among
                 several selection techniques, tournament selection is
                 often considered the most popular. Standard tournament
                 selection randomly selects a set of individuals from
                 the population and the individual with the best fitness
                 value is chosen as the winner. However, an opportunity
                 exists to enhance tournament selection as the standard
                 approach ignores finer-grained semantics which can be
                 collected during GP program execution. In the case of
                 symbolic regression problems, the error vectors on the
                 training fitness cases can be used in a more detailed
                 quantitative comparison. In this paper we introduce the
                 use of a statistical test into GP tournament selection
                 that uses information from the individual's error
                 vector, and three variants of the selection strategy
                 are proposed. We tested these methods on twenty five
                 regression problems and their noisy variants. The
                 experimental results demonstrate the benefit of the
                 proposed methods in reducing GP code growth and
                 improving the generalisation behaviour of GP solutions
                 when compared to standard tournament selection, a
                 similar selection technique and a state of the art
                 bloat control approach.",

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