Discovery of Search Objectives in Continuous Domains

Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@InProceedings{Liskowski:2017:GECCO,
  author =       "Pawel Liskowski and Krzysztof Krawiec",
  title =        "Discovery of Search Objectives in Continuous Domains",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "969--976",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071344",
  DOI =          "doi:10.1145/3071178.3071344",
  acmid =        "3071344",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, machine
                 learning, multiobjective optimization, nonnegative
                 matrix factorization",
  month =        "15-19 " # jul,
  abstract =     "In genetic programming (GP), the outcomes of the
                 evaluation phase can be represented as an interaction
                 matrix, with rows corresponding to programs in a
                 population and columns corresponding to tests that
                 define a program synthesis task. Recent contributions
                 on Discovery of Objectives via Clustering (DOC) and
                 Discovery of Objectives by Factorization of interaction
                 matrix (DOF) show that informative characterizations of
                 programs can be automatically derived from interaction
                 matrices in discrete domains and used as search
                 objectives in multidimensional setting. In this paper,
                 we propose analogous methods for continuous domains and
                 compare them with conventional GP that uses tournament
                 selection, Age-Fitness Pareto Optimization, and GP with
                 epsilon-lexicase selection. Experiments show that the
                 proposed methods are effective for symbolic regression,
                 systematically producing better-fitting models than the
                 two former baselines, and surpassing epsilon-lexicase
                 selection on some problems. We also investigate the
                 hybrids of the proposed approach with the baselines,
                 concluding that hybridization of DOC with
                 epsilon-lexicase leads to the best overall results.",
  notes =        "Also known as
                 \cite{Liskowski:2017:DSO:3071178.3071344} 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 Pawel Liskowski Krzysztof Krawiec

Citations