Linear Combination of Distance Measures for Surrogate Models in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{Zaefferer:2018:PPSN,
  author =       "Martin Zaefferer and Joerg Stork and Oliver Flasch and 
                 Thomas Bartz-Beielstein",
  title =        "Linear Combination of Distance Measures for Surrogate
                 Models in Genetic Programming",
  booktitle =    "15th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2018",
  editor =       "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and 
                 Penousal Machado and Luis Paquete and Darrell Whitley",
  volume =       "11102",
  series =       "LNCS",
  pages =        "220--231",
  address =      "Coimbra, Portugal",
  month =        "8-12 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Surrogate
                 models, Distance measures",
  isbn13 =       "978-3-319-99258-7",
  URL =          "https://www.springer.com/gp/book/9783319992587",
  DOI =          "doi:10.1007/978-3-319-99259-4_18",
  abstract =     "Surrogate models are a well established approach to
                 reduce the number of expensive function evaluations in
                 continuous optimization. In the context of genetic
                 programming, surrogate modelling still poses a
                 challenge, due to the complex genotype-phenotype
                 relationships. We investigate how different genotypic
                 and phenotypic distance measures can be used to learn
                 Kriging models as surrogates. We compare the measures
                 and suggest to use their linear combination in a
                 kernel.

                 We test the resulting model in an optimization
                 framework, using symbolic regression problem instances
                 as a benchmark. Our experiments show that the model
                 provides valuable information. Firstly, the model
                 enables an improved optimization performance compared
                 to a model-free algorithm. Furthermore, the model
                 provides information on the contribution of different
                 distance measures. The data indicates that a phenotypic
                 distance measure is important during the early stages
                 of an optimization run when less data is available. In
                 contrast, genotypic measures, such as the tree edit
                 distance, contribute more during the later stages.",
  notes =        "See also arXiv:1807.01019

                 PPSN2018 http://ppsn2018.dei.uc.pt

                 This two-volume set LNCS 11101 and 11102 constitutes
                 the refereed proceedings of the 15th International
                 Conference on Parallel Problem Solving from Nature,
                 PPSN 2018",
}

Genetic Programming entries for Martin Zaefferer Joerg Stork Oliver Flasch Thomas Bartz-Beielstein

Citations