Tree Swarm Optimization: An Approach to PSO-based Tree Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Christian Veenhuis and Mario K{\"o}ppen and 
                 J{\"o}rg Kr{\"u}ger and Bertram Nickolay",
  title =        "Tree Swarm Optimization: An Approach to PSO-based Tree
  booktitle =    "Proceedings of the 2005 IEEE Congress on Evolutionary
  year =         "2005",
  editor =       "David Corne and Zbigniew Michalewicz and 
                 Marco Dorigo and Gusz Eiben and David Fogel and Carlos Fonseca and 
                 Garrison Greenwood and Tan Kay Chen and 
                 Guenther Raidl and Ali Zalzala and Simon Lucas and Ben Paechter and 
                 Jennifier Willies and Juan J. Merelo Guervos and 
                 Eugene Eberbach and Bob McKay and Alastair Channon and 
                 Ashutosh Tiwari and L. Gwenn Volkert and 
                 Dan Ashlock and Marc Schoenauer",
  volume =       "2",
  pages =        "1238--1245",
  address =      "Edinburgh, UK",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "2-5 " # sep,
  organisation = "IEEE Computational Intelligence Society, Institution
                 of Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, PSO, TSO",
  ISBN =         "0-7803-9363-5",
  DOI =          "doi:10.1109/CEC.2005.1554832",
  abstract =     "In recent years a swarm-based optimisation methodology
                 called Particle Swarm Optimisation (PSO) has developed.
                 PSO is highly explorative and primarily used in
                 function optimisation. proposes a swarm-based learning
                 algorithm based on PSO which is able to discover trees
                 in tree spaces. Particles are flying through a tree
                 space forming flocks around peaks of a fitness
                 function. Because it inherits the explorative property
                 of PSO, it needs only few evaluations to find suitable
  notes =        "CEC2005 - A joint meeting of the IEEE, the IEE, and
                 the EPS.

                 symbolic regression, artificial ant, TSO. 'Tree swarm
                 optimisation uses full trees for the particles.' All
                 members of the swarm have the same structure and
                 length. Number of dimensions in continious PSO space =
                 size of terminal set + size of function set. No attempt
                 at placing similar symbols near each other. 'position
                 trees' and 'velocity trees'. Section 3.5 'Distances
                 between full symbol trees' Four symbolic regression.
                 'But as in PSO the exact global optimum cannot be found
                 very often for difficult problems.' Santa Fe artificial
                 ant. 'In 64 percent of the runs, solutions collecting
                 all food items are found.' UCI iris",

Genetic Programming entries for Christian Veenhuis Mario Koppen J\"org Kr\"uger Bertram Nickolay