Exploring extended particle swarms: a genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Riccardo Poli and Cecilia {Di Chio} and 
                 William B. Langdon",
  title =        "Exploring extended particle swarms: a genetic
                 programming approach",
  booktitle =    "{GECCO 2005}: Proceedings of the 2005 conference on
                 Genetic and evolutionary computation",
  year =         "2005",
  editor =       "Hans-Georg Beyer and Una-May O'Reilly and 
                 Dirk V. Arnold and Wolfgang Banzhaf and Christian Blum and 
                 Eric W. Bonabeau and Erick Cantu-Paz and 
                 Dipankar Dasgupta and Kalyanmoy Deb and James A. Foster and 
                 Edwin D. {de Jong} and Hod Lipson and Xavier Llora and 
                 Spiros Mancoridis and Martin Pelikan and Guenther R. Raidl and 
                 Terence Soule and Andy M. Tyrrell and 
                 Jean-Paul Watson and Eckart Zitzler",
  volume =       "1",
  ISBN =         "1-59593-010-8",
  pages =        "169--176",
  address =      "Washington DC, USA",
  URL =          "http://www.cs.essex.ac.uk/staff/poli/papers/geccopso2005.pdf",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2005/docs/p169.pdf",
  DOI =          "doi:10.1145/1068009.1068036",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, 10286-1405, USA",
  month =        "25-29 " # jun,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Swarm
                 Intelligence, particle swarm optimisation, PSO,
  size =         "8 pages",
  abstract =     "Particle Swarm Optimisation (PSO) uses a population of
                 particles fly over the fitness landscape in search of
                 an optimal solution. The particles are controlled by
                 forces that encourage each particle to fly back both
                 towards the best point sampled by it and towards the
                 swarm's best point, while its momentum tries to keep it
                 moving in its current direction.

                 Previous research \cite{poli:2005:eurogp} started
                 exploring the possibility of evolving the force
                 generating equations which control the particles
                 through the use of genetic programming (GP).

                 We independently verify the findings of
                 \cite{poli:2005:eurogp} and then extend it by
                 considering additional meaningful ingredients for the
                 PSO force-generating equations, such as global measures
                 of dispersion and position of the swarm. We show that,
                 on a range of problems, GP can automatically generate
                 new PSO algorithms that outperform standard
                 human-generated as well as some previously evolved
  notes =        "GECCO-2005 A joint meeting of the fourteenth
                 international conference on genetic algorithms
                 (ICGA-2005) and the tenth annual genetic programming
                 conference (GP-2005).

                 ACM Order Number 910052, XPS, ACM gecco-2005 key

Genetic Programming entries for Riccardo Poli Cecilia Di Chio William B Langdon