Understanding Particle Swarm Optimisation by Evolving Problem Landscapes

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

@InProceedings{langdon:2005:SIS,
  author =       "W. B. Langdon and Riccardo Poli and Owen Holland and 
                 Thiemo Krink",
  title =        "Understanding Particle Swarm Optimisation by Evolving
                 Problem Landscapes",
  booktitle =    "Proceedings SIS 2005 IEEE Swarm Intelligence",
  year =         "2005",
  editor =       "Luca Maria Gambardella and Payman Arabshahi and 
                 Alcherio Martinoli",
  pages =        "30--37",
  address =      "Pasadena, California, USA",
  month =        "8-10 " # jun,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, XPS",
  ISBN =         "0-7803-8917-4",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2005_SIS.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/langdon_2005_SIS.ps.gz",
  DOI =          "doi:10.1109/SIS.2005.1501599",
  size =         "8 pages",
  abstract =     "Genetic programming (GP) is used to create fitness
                 landscapes which highlight strengths and weaknesses of
                 different types of PSO and to contrast population-based
                 swarm approaches with non stochastic gradient followers
                 (i.e. hill climbers). These automatically generated
                 benchmark problems yield insights into the operation of
                 PSOs, illustrate benefits and drawbacks of different
                 population sizes and constriction (friction)
                 coefficients, and reveal new swarm phenomena such as
                 deception and the exploration/exploitation tradeoff.
                 The method could be applied to any type of optimizer.",
}

Genetic Programming entries for William B Langdon Riccardo Poli Owen Holland Thiemo Krink

Citations