Application of Fixed-Structure Genetic Programming for Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/icira/WuM12,
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author = "Xiaojun Wu and Yue Ma",
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title = "Application of Fixed-Structure Genetic Programming for
Classification",
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booktitle = "Proceedings of the 5th International Conference
Intelligent Robotics and Applications, ICIRA 2012, Part
I",
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year = "2012",
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editor = "Chun-Yi Su and Subhash Rakheja and Honghai Liu",
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volume = "7506",
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series = "Lecture Notes in Computer Science",
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pages = "22--33",
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address = "Montreal, Canada",
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month = oct # " 3-5",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, Classifier
systems, Data mining, Classification boundary",
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isbn13 = "978-3-642-33508-2",
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DOI = "doi:10.1007/978-3-642-33509-9_3",
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bibdate = "2012-10-14",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/icira/icira2012-1.html#WuM12",
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size = "12 pages",
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abstract = "There are three improvements based on GP algorithm in
this paper and a fixed-structure GP algorithm for
classification was proposed. Traditional GP algorithm
relies on non-fixed-length tree structure to describe
the classification problems. This algorithm uses a
fixed-length linear structure instead of the
traditional structure and optimises the leaf nodes
coefficients based on the hill-climbing algorithm.
Meanwhile, aiming at the samples on the classification
boundaries, an optimisation method of classification
boundaries is proposed which makes the classification
boundaries continuously tend to the optimal solutions
in the program evolution process. At the end, an
experiment is made by using this improved algorithm and
a two-categories sample set with classification
boundary is correctly classified (This sample set is an
accurate data set from UCI database) Then it shows the
analysis of classification results and the
classification model produced by this algorithm. The
experimental results indicates that the GP
classification algorithm with fixed structure could
improve the classification accuracy rate and accelerate
the solutions convergence speed, which is of great
significance in the practical application of
classification systems based on GP algorithm.",
- }
Genetic Programming entries for
Xiaojun Wu
Yue Ma
Citations