Genetic programming with data migration for symbolic regression

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

@InProceedings{Kommenda:2014:GECCOcomp,
  author =       "Michael Kommenda and Michael Affenzeller and 
                 Bogdan Burlacu and Gabriel Kronberger and Stephan M. Winkler",
  title =        "Genetic programming with data migration for symbolic
                 regression",
  booktitle =    "GECCO 2014 Workshop on Symbolic Regression and
                 Modelling",
  year =         "2014",
  editor =       "Steven Gustafson and Ekaterina Vladislavleva",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1361--1366",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2609857",
  DOI =          "doi:10.1145/2598394.2609857",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this publication genetic programming (GP) with data
                 migration for symbolic regression is presented. The
                 motivation for the development of the algorithm is to
                 evolve models which generalise well on previously
                 unseen data. GP with data migration uses multiple
                 subpopulations to maintain the genetic diversity during
                 the algorithm run and a sophisticated training subset
                 selection strategy. Each subpopulation is evaluated on
                 a different fixed training subset (FTS) and
                 additionally a variable training subset (VTS) is
                 exchanged between the subpopulations at specific data
                 migration intervals. Thus, the individuals are
                 evaluated on the unification of FTS and VTS and should
                 have better generalisation properties due to the
                 regular changes of the VTS.

                 The implemented algorithm is compared to several GP
                 variants on a number of symbolic regression benchmark
                 problems to test the effectiveness of the multiple
                 populations and data migration strategy. Additionally,
                 different algorithm configurations and migration
                 strategies are evaluated to show their impact with
                 respect to the achieved quality.",
  notes =        "Also known as \cite{2609857} Distributed at
                 GECCO-2014.",
}

Genetic Programming entries for Michael Kommenda Michael Affenzeller Bogdan Burlacu Gabriel Kronberger Stephan M Winkler

Citations