Extending Particle Swarm Optimisation via Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Riccardo Poli and William B. Langdon and 
                 Owen Holland",
  editor =       "Maarten Keijzer and Andrea Tettamanzi and 
                 Pierre Collet and Jano I. {van Hemert} and Marco Tomassini",
  title =        "Extending Particle Swarm Optimisation via Genetic
  booktitle =    "Proceedings of the 8th European Conference on Genetic
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3447",
  year =         "2005",
  address =      "Lausanne, Switzerland",
  month =        "30 " # mar # " - 1 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-25436-6",
  pages =        "291--300",
  publisher_address = "Berlin",
  URL =          "http://www.cs.essex.ac.uk/staff/poli/papers/eurogpPSO2005.pdf",
  URL =          "http://xps-swarm.essex.ac.uk/eurogp05.pdf",
  DOI =          "doi:10.1007/b107383",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  size =         "10 pages",
  abstract =     "Genetic programming is used to evolve Particle Swarm
                 Optimisers (PSOs). PSOs include a small number of
                 interacting particles, which fly over the fitness
                 landscape in search for high fitness points. The
                 particles are typically controlled by forces which
                 encourage each particle to fly back towards the best
                 point on the landscape sampled by it (personal best)
                 while at the same time trying to imitate the best
                 particle in the swarm with a drive towards the swarm's
                 best. The standard PSO is well known for its
                 effectiveness on a variety of optimisation problems. We
                 explore the possibility of evolving the force
                 generating equations to control the particles in a PSO.
                 Our aim is to verify the feasibility of this approach
                 and to start exploring what types of PSOs are most
                 appropriate for different classes of landscapes.",
  notes =        "Also known as eurogp:PoliLH05

                 Part of \cite{keijzer:2005:GP} EuroGP'2005 held in
                 conjunction with EvoCOP2005 and EvoWorkshops2005",

Genetic Programming entries for Riccardo Poli William B Langdon Owen Holland