TREAD: A new genetic programming representation aimed at research of long term complexity growth

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Tony E. Lewis and George D. Magoulas",
  title =        "TREAD: A new genetic programming representation aimed
                 at research of long term complexity growth",
  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 =        "1339--1340",
  address =      "Atlanta, GA, USA",
  URL =          "",
  DOI =          "doi:10.1145/1389095.1389353",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, artificial
                 intelligence, representations: Poster, TREAD",
  abstract =     "Several forms of computer program (or representation)
                 have been proposed for Genetic Programming (GP) systems
                 to evolve, such as linear, tree based or graph based.
                 Typically, GP representations are highly effective
                 during the initial search phases of evolution but
                 stagnate before deep levels of complexity are acquired.
                 A new representation, TREAD, is proposed to combine
                 aspects of flow of execution and flow of data systems.
                 The distinguishing features of TREAD are designed for
                 researching improvements to the long term acquisition
                 of novel features in GP (at the expense of the speed of
                 the initial search if necessary). TREAD is validated on
                 a symbolic regression problem and is found to be
                 capable of successfully developing solutions through
                 artificial evolution.",
  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

                 PADO \cite{teller:1995:PADO}. Data flow, flow of
                 execution. PDGP.",

Genetic Programming entries for Tony Lewis George D Magoulas