GP for Object Classification: Brood Size in Brood Recombination Crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{DBLP:conf/ausai/ZhangGL06,
  author =       "Mengjie Zhang and Xiaoying Gao and Weijun Lou",
  title =        "GP for Object Classification: Brood Size in Brood
                 Recombination Crossover",
  booktitle =    "Australian Conference on Artificial Intelligence",
  year =         "2006",
  pages =        "274--284",
  DOI =          "doi:10.1007/11941439_31",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  editor =       "Abdul Sattar and Byeong Ho Kang",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "4304",
  ISBN =         "3-540-49787-0",
  address =      "Hobart, Australia",
  month =        dec # " 4-8",
  keywords =     "genetic algorithms, genetic programming",
  size =         "11 pages",
  abstract =     "The brood size plays an important role in the brood
                 recombination crossover method in genetic programming.
                 However, there has not been any thorough investigation
                 on the brood size and the methods for setting this size
                 have not been effectively examined. This paper
                 investigates a number of new developments of brood size
                 in the brood recombination crossover method in GP. We
                 first investigate the effect of different fixed brood
                 sizes, then construct three dynamic models for setting
                 the brood size. These developments are examined and
                 compared with the standard crossover operator on three
                 object classification problems of increasing
                 difficulty. The results suggest that the brood
                 recombination methods with all the new developments
                 outperforms the standard crossover operator for all the
                 problems. As the brood size increases, the system
                 effective performance can be improved. When it exceeds
                 a certain point, however, the effective performance
                 will not be improved and the system will become less
                 efficient. Investigation of three dynamic models for
                 the brood size reveals that a good variable brood size
                 which is dynamically set with the number of generations
                 can further improve the system performance over the
                 fixed brood sizes.",
}

Genetic Programming entries for Mengjie Zhang Xiaoying (Sharon) Gao Weijun (Norman) Lou

Citations