Looseness Controlled Crossover in GP for Object Recognition

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

  author =       "Mengjie Zhang and Xiaoying Gao and Weijun Lou",
  title =        "Looseness Controlled Crossover in GP for Object
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "4428--4435",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/CEC.2006.1688457",
  size =         "8 pages",
  abstract =     "improving the crossover operator in genetic
                 programming for object recognition particularly object
                 classification problems. In this approach, instead of
                 randomly choosing the crossover points as in the
                 standard crossover operator, we use a measure called
                 looseness to guide the selection of crossover points.
                 Rather than using the genetic beam search only, this
                 approach uses a hybrid beam-hill climbing search scheme
                 in the evolutionary process. This approach is examined
                 and compared with the standard crossover operator and
                 the headless chicken crossover method on a sequence of
                 object classification problems. The results suggest
                 that this approach outperforms both the headless
                 chicken crossover and the standard crossover on all of
                 these problems.",
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",

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