Evolutionary Program Sketching

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Bladek:2017:EuroGP,
  author =       "Iwo Bladek and Krzysztof Krawiec",
  title =        "Evolutionary Program Sketching",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  volume =       "10196",
  series =       "LNCS",
  pages =        "3--18",
  address =      "Amsterdam",
  month =        "19-21 " # apr,
  organisation = "Species",
  publisher =    "Springer Verlag",
  note =         "Forthcoming",
  keywords =     "genetic algorithms, genetic programming, program
                 synthesis, satisfiability modulo theory, program
                 sketching",
  URL =          "http://repozytorium.put.poznan.pl/publication/495662",
  DOI =          "doi:10.1007/978-3-319-55696-3_1",
  size =         "16 pages",
  abstract =     "Program synthesis can be posed as a satisfiability
                 problem and approached with generic SAT solvers. Only
                 short programs can be however synthesized in this way.
                 Program sketching by Solar-Lezama assumes that a human
                 provides a partial program (sketch), and that synthesis
                 takes place only within the uncompleted parts of that
                 program. This allows synthesizing programs that are
                 overall longer, while maintaining manageable
                 computational effort. In this paper, we propose
                 Evolutionary Program Sketching (EPS), in which the role
                 of sketch provider is handed over to genetic
                 programming (GP). A GP algorithm evolves a population
                 of partial programs, which are being completed by a
                 solver while evaluated. We consider several variants of
                 EPS, which vary in program terminals used for
                 completion (constants, variables, or both) and in the
                 way the completion outcomes are propagated to future
                 generations. When applied to a range of benchmarks, EPS
                 outperforms the conventional GP, also when the latter
                 is given similar time budget.",
  notes =        "raport z badan

                 Part of \cite{Castelli:2017:GP} EuroGP'2017 held in
                 conjunction with EvoCOP2017, EvoMusArt2017 and
                 EvoApplications2017",
}

Genetic Programming entries for Iwo Bladek Krzysztof Krawiec

Citations