Tracking Extrema in Dynamic Environments

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Peter J. Angeline",
  title =        "Tracking Extrema in Dynamic Environments",
  booktitle =    "Proceedings of the 6th International Conference on
                 Evolutionary Programming",
  year =         "1997",
  editor =       "P. J. Angeline and R. G. Reynolds and 
                 J. R. McDonnell and R. Eberhart",
  volume =       "1213",
  series =       "Lecture Notes in Computer Science",
  pages =        "335--345",
  address =      "Indianapolis, Indiana, USA",
  month =        apr # " 13-16",
  publisher =    "Springer Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-62788-X",
  URL =          "",
  DOI =          "doi:10.1007/BFb0014823",
  size =         "11 pages",
  abstract =     "Typical applications of evolutionary optimization
                 involve the off-line approximation of extrema of static
                 multi-modal functions. Methods which use a variety of
                 techniques to self-adapt mutation parameters have been
                 shown to be more successful than methods which do not
                 use self-adaptation. For dynamic functions, the
                 interest is not to obtain the extrema but to follow it
                 as closely as possible. This paper compares the on-line
                 extrema tracking performance of an evolutionary program
                 without self-adaptation against an evolutionary program
                 using a self-adaptive Gaussian update rule over a
                 number of dynamics applied to a simple static function.
                 The experiments demonstrate that for some dynamic
                 functions, self-adaptation is effective while for
                 others it is detrimental.",
  notes =        "EP-97",

Genetic Programming entries for Peter John Angeline