A New Crossover Operator in Genetic Programming for Object Classification

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

@Article{Zhang:2007:SMC,
  author =       "Mengjie Zhang and Xiaoying Gao and Weijun Lou",
  title =        "A New Crossover Operator in Genetic Programming for
                 Object Classification",
  journal =      "IEEE Transactions on Systems, Man and Cybernetics,
                 Part B",
  year =         "2007",
  volume =       "37",
  number =       "5",
  pages =        "1332--1343",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/TSMCB.2007.902043",
  ISSN =         "1083-4419",
  abstract =     "The crossover operator has been considered, the centre
                 of the storm, in genetic programming (GP). However,
                 many existing GP approaches to object recognition
                 suggest that the standard GP crossover is not
                 sufficiently powerful in producing good child programs
                 due to the totally random choice of the crossover
                 points. To deal with this problem, this paper
                 introduces an approach with a new crossover operator in
                 GP for object recognition, particularly object
                 classification. In this approach, a local hill-climbing
                 search is used in constructing good building blocks, a
                 weight called looseness is introduced to identify the
                 good building blocks in individual programs, and the
                 looseness values are used as heuristics in choosing
                 appropriate crossover points to preserve good building
                 blocks. This approach is examined and compared with the
                 standard crossover operator and the headless chicken
                 crossover (HCC) method on a sequence of object
                 classification problems. The results suggest that this
                 approach outperforms the HCC, the standard crossover,
                 and the standard crossover operator with hill climbing
                 on all of these problems in terms of the classification
                 accuracy. Although this approach spends a bit longer
                 time than the standard crossover operator, it
                 significantly improves the system efficiency over the
                 HCC method.",
}

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

Citations