Evolving Problems to Learn about Particle Swarm and other Optimisers

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "William B. Langdon and Riccardo Poli",
  title =        "Evolving Problems to Learn about Particle Swarm and
                 other Optimisers",
  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 =       "1",
  pages =        "81--88",
  address =      "Edinburgh, UK",
  month =        "2-5 " # sep,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, PSO",
  ISBN =         "0-7803-9363-5",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2005.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2005.ps.gz",
  DOI =          "doi:10.1109/CEC.2005.1554670",
  size =         "8 pages",
  abstract =     "We use evolutionary computation (EC) to automatically
                 find problems which demonstrate the strength and
                 weaknesses of modern search heuristics. In particular
                 we analyse Particle Swarm Optimization (PSO) and
                 Differential Evolution (DE). Both evolutionary
                 algorithms are contrasted with a robust deterministic
                 gradient based searcher (based on Newton-Raphson). The
                 fitness landscapes made by genetic programming (GP) are
                 used to illustrate difficulties in GAs and PSOs thereby
                 explaining how they work and allowing us to devise
                 better extended particle swarm systems (XPS).",
  notes =        "CEC2005 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 Shorter version available as

Genetic Programming entries for William B Langdon Riccardo Poli