Lexicase selection promotes effective search and behavioural diversity of solutions in Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{oksanen:2017:CEC,
  author =       "Karoliina Oksanen and Ting Hu",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Lexicase selection promotes effective search and
                 behavioural diversity of solutions in Linear Genetic
                 Programming",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "169--176",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "Linear Genetic Programming (LGP) is an evolutionary
                 algorithm aimed at solving computational problems, most
                 common problem types being symbolic regression and
                 classification. The standard method for selecting the
                 parent individuals that get to undergo modification at
                 each generation of the algorithm is tournament
                 selection, which operates based on an aggregate fitness
                 value computed on the whole training dataset. Lexicase
                 selection, a novel parent selection method introduced
                 by Lee Spector and his research group, works
                 differently by randomly ordering the samples in the
                 training dataset and using each of them in turn to
                 eliminate parent candidates from consideration. As a
                 result it allows for selecting specialist individuals,
                 which perform well on some samples but badly on others,
                 instead of generalist individuals whose average
                 performance on all of the samples is good. Lexicase
                 selection has previously been tested on tree-GP and
                 PushGP, but not on LGP. In this study, we use three
                 different benchmark problems to compare its performance
                 to tournament selection, investigating the mean best
                 fitness values of the test runs at each generation, as
                 well as the effect of the parent selection operator on
                 behavioural diversity. We conclude that lexicase
                 selection drives the search towards good solutions more
                 effectively than tournament selection, and that this
                 effect correlates with improved behavioural diversity
                 in most cases.",
  keywords =     "genetic algorithms, genetic programming, linear
                 programming, LGP, Lee Spector, aggregate fitness value,
                 behavioural diversity, evolutionary algorithm, lexicase
                 selection, linear genetic programming, parent
                 candidates, parent selection method, parent selection
                 operator, standard method, symbolic regression,
                 tournament selection, Benchmark testing, Registers,
                 Sociology, Spirals, Standards, Statistics",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969310",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969310}",
}

Genetic Programming entries for Karoliina Oksanen Ting Hu

Citations