Adapted Geometric Semantic Genetic programming for diabetes and breast cancer classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Zhu:2013:MLSP,
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author = "Zhechen Zhu and Asoke K. Nandi and
Muhammad Waqar Aslam",
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title = "Adapted Geometric Semantic Genetic programming for
diabetes and breast cancer classification",
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booktitle = "IEEE International Workshop on Machine Learning for
Signal Processing (MLSP 2013)",
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year = "2013",
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month = sep,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/MLSP.2013.6661969",
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ISSN = "1551-2541",
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abstract = "In this paper, we explore new Adapted Geometric
Semantic (AGS) operators in the case where Genetic
programming (GP) is used as a feature generator for
signal classification. Also to control the
computational complexity, a devolution scheme is
introduced to reduce the solution complexity without
any significant impact on their fitness. Fisher's
criterion is employed as fitness function in GP. The
proposed method is tested using diabetes and breast
cancer datasets. According to the experimental results,
GP with AGS operators and devolution mechanism provides
better classification performance while requiring less
training time as compared to standard GP.",
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notes = "Also known as \cite{6661969}",
- }
Genetic Programming entries for
Zhechen Zhu
Asoke K Nandi
Muhammad Waqar Aslam
Citations