Finding Social Landscapes for PSOs via Kernels

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{langdon:2006:CEC,
  author =       "William B. Langdon and Riccardo Poli",
  title =        "Finding Social Landscapes for {PSO}s via Kernels",
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
                 Computation",
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "6118--6125",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, PSO, XPS",
  ISBN =         "0-7803-9487-9",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2006.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/wbl_cec2006.ps.gz",
  URL =          "http://ieeexplore.ieee.org/iel5/11108/35623/01688507.pdf?tp=&arnumber=1688507&isnumber=35623",
  size =         "8 pages",
  abstract =     "Particle swarm optimiser and genetic algorithm
                 populations are macro-organisms, which perceive their
                 environment as if filtered via a kernel. The kernel
                 assimilates each individual's sensory abilities so that
                 the collective moves using a greedy hill-climbing
                 strategy. This model is fitted to data collected in
                 real PSO and GA runs by using genetic programming to
                 evolve the kernel.

                 In nature animals tend to live within groups. The
                 social interactions effectively transform the fitness
                 selection landscape seen by an isolated individual. In
                 some cases a group behaves (or even can be said to
                 think) like a single organism. Kernels provide a lens
                 which coarse-grains or averages individual senses and
                 so may help explain joint actions and social
                 responses.

                 The original multi-modal problem is smoothed by
                 convolving it with a problem specific filter designed
                 by GP. Because populations see the transformed social
                 fitness landscape, they can pass over local optima. GP
                 can give a good fit between the predicted behaviour of
                 the macroscopic organism and the actual runs.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",
}

Genetic Programming entries for William B Langdon Riccardo Poli