Constituent Grammatical Evolution

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

@InProceedings{Georgiou:2011:IJCAI,
  author =       "Loukas Georgiou and William J. Teahan",
  title =        "Constituent Grammatical Evolution",
  booktitle =    "Proceedings of the Twenty-Second International Joint
                 Conference on Artificial Intelligence",
  year =         "2011",
  editor =       "Toby Walsh",
  pages =        "1261--1268",
  address =      "Barcelona, Spain",
  publisher_address = "Menlo Park, California, USA",
  month =        "16-22 " # jul,
  organisation = "International Joint Conferences on Artificial
                 Intelligence",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  isbn13 =       "978-1-57735-512-0",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.208.1709",
  URL =          "http://ijcai.org/papers11/Papers/IJCAI11-214.pdf",
  size =         "8 pages",
  abstract =     "We present Constituent Grammatical Evolution (CGE), a
                 new evolutionary automatic programming algorithm that
                 extends the standard Grammatical Evolution algorithm by
                 incorporating the concepts of constituent genes and
                 conditional behaviour-switching. CGE builds from
                 elementary and more complex building blocks a control
                 program which dictates the behaviour of an agent and it
                 is applicable to the class of problems where the
                 subject of search is the behaviour of an agent in a
                 given environment. It takes advantage of the powerful
                 Grammatical Evolution feature of using a BNF grammar
                 definition as a plug-in component to describe the
                 output language to be produced by the system. The main
                 benchmark problem in which CGE is evaluated is the
                 Santa Fe Trail problem using a BNF grammar definition
                 which defines a search space semantically equivalent
                 with that of the original definition of the problem by
                 Koza. Furthermore, CGE is evaluated on two additional
                 problems, the Los Altos Hills and the Hampton Court
                 Maze. The experimental results demonstrate that
                 Constituent Grammatical Evolution outperforms the
                 standard Grammatical Evolution algorithm in these
                 problems, in terms of both efficiency (percent of
                 solutions found) and effectiveness (number of required
                 steps of solutions found).",
  notes =        "Santa Fe Ant, Lost Altos Hills, Hampton Court Maze,
                 jGE http://ijcai.org/papers11/contents.php",
}

Genetic Programming entries for Loukas Georgiou William J Teahan

Citations