Decomposition-based Multi-objective Genetic Programming for Feature Learning in Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{zhang:2024:GECCOcomp5,
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author = "Tuo Zhang and Ying Bi and Jing Liang and Bing Xue and
Mengjie Zhang",
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title = "Decomposition-based Multi-objective Genetic
Programming for Feature Learning in Image
Classification",
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booktitle = "Proceedings of the 2024 Genetic and Evolutionary
Computation Conference Companion",
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year = "2024",
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editor = "Ting Hu and Aniko Ekart",
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pages = "555--558",
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address = "Melbourne, Australia",
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series = "GECCO '24",
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month = "14-18 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming,
multi-objective optimization, feature learning, image
classification, evolutionary computation: Poster",
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isbn13 = "979-8-4007-0495-6",
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DOI = "doi:10.1145/3638530.3654306",
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size = "4 pages",
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abstract = "Image classification is challenging due to the high
dimensionality and large variations of the image data.
As an effective method in image classification, feature
learning can be considered a multiobjective problem of
maximizing the classification accuracy and minimizing
the number of learned features. Existing
multi-objective genetic programming (MOGP) methods
directly apply the multiobjective techniques to GP
without considering the characteristics of feature
learning tasks. Therefore, this paper proposes a
decomposition-based MOGP approach with a global
replacement strategy to feature learning in image
classification. The proposed approach is compared with
existing MOGP methods and the experimental results
demonstrate the effectiveness of the proposed
approach.",
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notes = "GECCO-2024 GP A Recombination of the 33rd
International Conference on Genetic Algorithms (ICGA)
and the 29th Annual Genetic Programming Conference
(GP)",
- }
Genetic Programming entries for
Tuo Zhang
Ying Bi
Jing Liang
Bing Xue
Mengjie Zhang
Citations