A New Crossover Operator in Genetic Programming for Object Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Zhang:2007:SMC,
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author = "Mengjie Zhang and Xiaoying Gao and Weijun Lou",
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title = "A New Crossover Operator in Genetic Programming for
Object Classification",
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journal = "IEEE Transactions on Systems, Man and Cybernetics,
Part B",
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year = "2007",
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volume = "37",
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number = "5",
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pages = "1332--1343",
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month = oct,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/TSMCB.2007.902043",
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ISSN = "1083-4419",
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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