Dynamic Observation of Genotypic and Phenotypic Diversity for Different Symbolic Regression GP Variants

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

@InProceedings{Affenzeller:2017:GECCO,
  author =       "Michael Affenzeller and Stephan M. Winkler and 
                 Bogdan Burlacu and Gabriel Kronberger and Michael Kommenda and 
                 Stefan Wagner",
  title =        "Dynamic Observation of Genotypic and Phenotypic
                 Diversity for Different Symbolic Regression {GP}
                 Variants",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "1553--1558",
  size =         "6 pages",
  URL =          "http://doi.acm.org/10.1145/3067695.3082530",
  DOI =          "doi:10.1145/3067695.3082530",
  acmid =        "3082530",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, genetic and
                 phenotypic diversity, offspring selection, population
                 dynamics, symbolic regression",
  month =        "15-19 " # jul,
  abstract =     "Understanding the relationship between selection,
                 genotype-phenotype map and loss of population diversity
                 represents an important step towards more effective
                 genetic programming (GP) algorithms. This paper
                 describes an approach to capture dynamic changes in
                 this relationship. We analyse the frequency
                 distribution of points in the diversity plane defined
                 by structural and semantic similarity measures. We test
                 our methodology using standard GP (SGP) on a number of
                 test problems, as well as Offspring Selection GP
                 (OS-GP), an algorithmic flavour where selection is
                 explicitly focused towards adaptive change. We end with
                 a discussion about the implications of diversity
                 maintenance for each of the tested algorithms. We
                 conclude that diversity needs to be considered in the
                 context of fitness improvement, and that more diversity
                 is not necessarily beneficial in terms of solution
                 quality.",
  notes =        "Also known as
                 \cite{Affenzeller:2017:DOG:3067695.3082530} GECCO-2017
                 A Recombination of the 26th International Conference on
                 Genetic Algorithms (ICGA-2017) and the 22nd Annual
                 Genetic Programming Conference (GP-2017)",
}

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

Citations