Non-negative Matrix Factorization for Unsupervised Derivation of Search Objectives in Genetic Programming

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

@InProceedings{Liskowski:2016:GECCO,
  author =       "Pawel Liskowski and Krzysztof Krawiec",
  title =        "Non-negative Matrix Factorization for Unsupervised
                 Derivation of Search Objectives in Genetic
                 Programming",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich",
  pages =        "749--756",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908888",
  abstract =     "In genetic programming (GP), the outcomes of the
                 evaluation phase in an evolutionary loop can be
                 represented as an interaction matrix, with rows
                 corresponding to programs in a population, columns
                 corresponding to tests that define a program synthesis
                 task, and ones and zeroes signalling respectively
                 passing a test and failing to do so. The conventional
                 fitness, equivalent to a row sum in that matrix, only
                 crudely reflects program's compliance with desired
                 output, and recent contributions in semantic and
                 behavioural GP point to alternative, multifaceted
                 characterizations that facilitate navigation in the
                 search space. In this paper, we propose DOF, a method
                 that uses the popular machine learning technique of
                 non-negative matrix factorization to heuristically
                 derive a low number of underlying objectives from an
                 interaction matrix. The resulting objectives redefine
                 the original single-objective synthesis problem as a
                 multiobjective optimization problem, and we posit that
                 such characterization fosters diversification of search
                 directions while maintaining useful search gradient.
                 The comparative experiment conducted on 15 problems
                 from discrete domains confirms this claim: DOF
                 outperforms the conventional GP and GP equipped with an
                 alternative method of derivation of search objectives
                 on success rate and convergence speed.",
  notes =        "Institute of Computing Science Poznan University of
                 Technology

                 GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",
}

Genetic Programming entries for Pawel Liskowski Krzysztof Krawiec

Citations