Accelerating genetic programming by frequent subtree mining

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

@InProceedings{Kameya:2008:gecco,
  author =       "Yoshitaka Kameya and Junichi Kumagai and 
                 Yoshiaki Kurata",
  title =        "Accelerating genetic programming by frequent subtree
                 mining",
  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 =        "1203--1210",
  address =      "Atlanta, GA, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1203.pdf",
  DOI =          "doi:10.1145/1389095.1389332",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, building
                 blocks, frequent subtree mining, probabilistic model
                 building genetic programming",
  abstract =     "One crucial issue in genetic programming (GP) is how
                 to acquire promising building blocks efficiently. In
                 this paper, we propose a GP method (called GPTM, GP
                 with Tree Mining) which protects the subtrees
                 repeatedly appearing in superior individuals. Currently
                 GPTM uses a FREQT-like efficient data mining method to
                 find such subtrees. GPTM is evaluated by three
                 benchmark problems, and the results indicate that GPTM
                 is comparable to or better than POLE, one of the most
                 advanced probabilistic model building GP methods, and
                 finds the optimal individual earlier than the standard
                 GP and POLE.",
  notes =        "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{1389332}

                 GPsys-2b, Java. Santa Fe Ant, Royal Tree.",
}

Genetic Programming entries for Yoshitaka Kameya Junichi Kumagai Yoshiaki Kurata

Citations