Semantic Search Techniques for Learning Smaller Boolean Expression Trees in Genetic Programming

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

@Article{journals/ijcia/MillerC14,
  author =       "Nicholas C. Miller and Philip K. Chan",
  title =        "Semantic Search Techniques for Learning Smaller
                 Boolean Expression Trees in Genetic Programming",
  journal =      "International Journal of Computational Intelligence
                 and Applications",
  year =         "2014",
  number =       "3",
  volume =       "13",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, semantic
                 search, boolean",
  ISSN =         "1469-0268",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.696.8161",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.696.8161",
  bibdate =      "2014-10-17",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/ijcia/ijcia13.html#MillerC14",
  URL =          "http://cs.fit.edu/~pkc/papers/ijcia14.pdf",
  DOI =          "doi:10.1142/S1469026814500187",
  size =         "17 pages",
  abstract =     "One sub-field of Genetic Programming (GP) which has
                 gained recent interest is semantic GP, in which
                 programs are evolved by manipulating program semantics
                 instead of program syntax. This paper introduces a new
                 semantic GP algorithm, called SGP+, which is an
                 extension of an existing algorithm called SGP. New
                 crossover and mutation operators are introduced which
                 address two of the major limitations of SGP: large
                 program trees and reduced accuracy on high-arity
                 problems. Experimental results on deceptive Boolean
                 problems show that programs created by the SGP+ are 3.8
                 times smaller while still maintaining accuracy as good
                 as, or better than, SGP. Additionally, a statistically
                 significant improvement in program accuracy is observed
                 for several high-arity Boolean problems.",
  notes =        "Department of Computer Sciences, Florida Institute of
                 Technology, 150 W. University Blvd., Melbourne, FL
                 32901, USA",
}

Genetic Programming entries for Nicholas C Miller Philip K Chan

Citations