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https://nagoya.repo.nii.ac.jp/oai
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Construction of image feature extractors based on multi-objective genetic programming with redundancy regulations
Watchareeruetai, Ukrit
Matsumoto, Tetsuya
Takeuchi, Yoshinori
Kudo, Hiroaki
Ohnishi, Noboru
open access
©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Multi-objective optimization
image feature extraction
linear genetic programming
non-dominated sorting
redundancy regulation
This paper proposes a multi-objective genetic programming (MOGP) for automatic construction of feature extraction programs (FEPs). The proposed method is modified from a well known non-dominated sorting evolutionary algorithm, i.e., NSGA-II. The key differences of the method are related with redundancies in program representation. We apply redundancy regulations in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity. Experimental results exhibit that the proposed MOGP-based FEPs construction system provides obviously better performance than the original non-dominated sorting approach.
IEEE
2009-10-11
eng
journal article
VoR
http://hdl.handle.net/2237/13923
https://nagoya.repo.nii.ac.jp/records/12047
https://doi.org/10.1109/ICSMC.2009.5346242 <br/>
1062-922X
IEEE International Conference on Systems, Man and Cybernetics (SMC 2009)
1328
1333
https://nagoya.repo.nii.ac.jp/record/12047/files/matsumoto.pdf
application/pdf
366.4 kB
2018-02-20