Enhanced generalized ant programming (EGAP)

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Amirali Salehi-Abari and Tony White",
  title =        "Enhanced generalized ant programming (EGAP)",
  booktitle =    "GECCO '08: Proceedings of the 10th annual conference
                 on Genetic and evolutionary computation",
  year =         "2008",
  editor =       "Maarten Keijzer and Giuliano Antoniol and 
                 Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and 
                 Nikolaus Hansen and John H. Holmes and 
                 Gregory S. Hornby and Daniel Howard and James Kennedy and 
                 Sanjeev Kumar and Fernando G. Lobo and 
                 Julian Francis Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Jordan Pollack and Kumara Sastry and 
                 Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and 
                 Ingo Wegener",
  isbn13 =       "978-1-60558-130-9",
  pages =        "111--118",
  address =      "Atlanta, GA, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p111.pdf",
  URL =          "http://doi.acm.org/10.1145/1389095.1389111",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, ant
                 programming, automatic programming, enhanced
                 generalised ant programming, generalised ant
                 programming, heuristic, Ant colony optimisation, swarm
                 intelligence, artificial immune systems",
  size =         "8 pages",
  abstract =     "This paper begins by reviewing different methods of
                 automatic programming while emphasising the technique
                 of Ant Programming (AP). AP uses an ant foraging
                 metaphor in which ants generate a program by moving
                 through a graph. Generalised Ant Programming (GAP) uses
                 a context-free grammar and an Ant Colony System (ACS)
                 to guide the program generation search process. There
                 are two enhancements to GAP that are proposed in this
                 paper. These are: providing a heuristic for path
                 termination inspired by building construction and a
                 novel pheromone placement algorithm. Three well-known
                 problems -- Quartic symbolic regression, multiplexer,
                 and an ant trail problem -- are experimentally compared
                 using enhanced GAP (EGAP) and GAP. The results of the
                 experiments show the statistically significant
                 advantage of using this heuristic function and
                 pheromone placement algorithm of EGAP over GAP.",
  notes =        "grammar, ACO, quartic polynomial, 4-to-1 multiplexor,
                 artificial ant (Santa Fe). Cited by

                 GECCO-2008 A joint meeting of the seventeenth
                 international conference on genetic algorithms
                 (ICGA-2008) and the thirteenth annual genetic
                 programming conference (GP-2008).

                 ACM Order Number 910081. Also known as \cite{1389111}",

Genetic Programming entries for Amirali Salehi-Abari Tony White