Genetic Programming for Dynamic Environments

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

  author =       "Zheng Yin and Anthony Brabazon and 
                 Conall O'Sullivan and Michael O'Neill",
  title =        "Genetic Programming for Dynamic Environments",
  booktitle =    "2nd International Symposium {"}Advances in Artificial
                 Intelligence and Applications{"}",
  year =         "2007",
  volume =       "2",
  pages =        "437--446",
  address =      "Wisla, Poland",
  month =        oct # " 15-17",
  keywords =     "genetic algorithms, genetic programming, dynamic
  ISSN =         "1896 7094",
  URL =          "",
  size =         "10 pages",
  abstract =     "Genetic Programming (GP) is an automated computational
                 programming methodology which is inspired by the
                 workings of natural evolution techniques. It has been
                 applied to solve complex problems in multiple
                 application domains. This paper investigates the
                 application of a dynamic form of GP in which the
                 probability of crossover and mutation adapts during the
                 GP run. This allows GP to adapt its
                 diversity-generating process during a run in response
                 to feedback from the fitness function. A proof of
                 concept study is then undertaken on the important
                 real-world problem of options pricing. The results
                 indicate that the dynamic form of GP yields better
                 results than are obtained from canonical GP with fixed
                 crossover and mutation rates. The developed method has
                 potential for implementation across a range of dynamic
                 problem environments.",

Genetic Programming entries for Zheng Yin Anthony Brabazon Conall O'Sullivan Michael O'Neill