Geometric Semantic Genetic Programming for Recursive Boolean Programs

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

@InProceedings{Moraglio:2017:GECCO,
  author =       "Alberto Moraglio and Krzysztof Krawiec",
  title =        "Geometric Semantic Genetic Programming for Recursive
                 Boolean Programs",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4920-8",
  address =      "Berlin, Germany",
  pages =        "993--1000",
  size =         "8 pages",
  URL =          "http://doi.acm.org/10.1145/3071178.3071266",
  DOI =          "doi:10.1145/3071178.3071266",
  acmid =        "3071266",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, boolean
                 functions, geometric semantic genetic programming,
                 principled design, recursive programs, semantics",
  month =        "15-19 " # jul,
  abstract =     "Geometric Semantic Genetic Programming (GSGP) induces
                 a unimodal fitness landscape for any problem that
                 consists in finding a function fitting given
                 input/output examples. Most of the work around GSGP to
                 date has focused on real-world applications and on
                 improving the originally proposed search operators,
                 rather than on broadening its theoretical framework to
                 new domains. We extend GSGP to recursive programs, a
                 notoriously challenging domain with highly
                 discontinuous fitness landscapes. We focus on programs
                 that map variable-length Boolean lists to Boolean
                 values, and design search operators that are provably
                 efficient in the training phase and attain perfect
                 generalization. Computational experiments complement
                 the theory and demonstrate the superiority of the new
                 operators to the conventional ones. This work provides
                 new insights into the relations between program syntax
                 and semantics, search operators and fitness landscapes,
                 also for more general recursive domains.",
  notes =        "Also known as \cite{Moraglio:2017:GSG:3071178.3071266}
                 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 Alberto Moraglio Krzysztof Krawiec

Citations