Counterexample-driven Genetic Programming

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

@InProceedings{Krawiec:2017:GECCOa,
  author =       "Krzysztof Krawiec and Iwo Bladek and Jerry Swan",
  title =        "Counterexample-driven Genetic Programming",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "953--960",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071224",
  DOI =          "doi:10.1145/3071178.3071224",
  acmid =        "3071224",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  note =         "Best paper",
  keywords =     "genetic algorithms, genetic programming, SAT, SMT,
                 counterexample, formal verification, program
                 synthesis",
  month =        "15-19 " # jul,
  abstract =     "Genetic programming is an effective technique for
                 inductive synthesis of programs from training examples
                 of desired input-output behaviour (tests). Programs
                 synthesized in this way are not guaranteed to
                 generalize beyond the training set, which is
                 unacceptable in many applications. We present
                 Counterexample-Driven Genetic Programming (CDGP) that
                 employs evolutionary search to synthesize provably
                 correct programs from formal specifications. CDGP
                 employs a Satisfiability Modulo Theories (SMT) solver
                 to formally verify programs in the evaluation phase. A
                 failed verification produces counterexamples that are
                 in turn used to calculate fitness and so drive the
                 search process. When compared against a range of
                 approaches on a suite of state-of-the-art
                 specification-based synthesis benchmarks, CDGP
                 systematically outperforms them, typically synthesizing
                 correct programs faster and using fewer tests.",
  notes =        "Also known as \cite{Krawiec:2017:CGP:3071178.3071224}
                 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 Krzysztof Krawiec Iwo Bladek Jerry Swan

Citations