A Variant Program Structure in Tree-Based Genetic Programming for Multiclass Object Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InCollection{Zhang:2009:EIASP,
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author = "Mengjie Zhang and Mark Johnston",
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title = "A Variant Program Structure in Tree-Based Genetic
Programming for Multiclass Object Classification",
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booktitle = "Evolutionary Image Analysis and Signal Processing",
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publisher = "Springer",
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year = "2009",
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editor = "Stefano Cagnoni",
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volume = "213",
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series = "Studies in Computational Intelligence",
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pages = "55--72",
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address = "Berlin / Heidelberg",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-01635-6",
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ISSN = "1860-949X",
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DOI = "doi:10.1007/978-3-642-01636-3_4",
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abstract = "This chapter describes an approach to the use of
genetic programming for multiclass object
classification. Instead of using the standard
tree-based genetic programming approach, where each
genetic program returns just one floating point number
that is then translated into different class labels,
this approach invents a new program structure with
multiple outputs, each for a particular class. A voting
scheme is then applied to these output values to
determine the class of the input object. The approach
is examined and compared with the standard genetic
programming approach on four multiclass object
classification tasks with increasing difficulty. The
results show that the new approach outperforms the
basic approach on these problems. A characteristic of
the proposed program structure is that it can easily
produce multiple outputs for multiclass object
classification problems, while still keeping the
advantages of the standard genetic programming approach
for easy crossover and mutation. This approach can
solve a multiclass object recognition problem using a
single evolved program in a single run.",
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notes = "EvoISAP, EvoNET, EvoStar",
- }
Genetic Programming entries for
Mengjie Zhang
Mark Johnston
Citations