A Comparative Analysis of Dimensionality Reduction Methods for Genetic Programming to Solve High-Dimensional Symbolic Regression Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Zhong:2021:SMC,
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author = "Lianjie Zhong and Jinghui Zhong and Chengyu Lu",
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booktitle = "2021 IEEE International Conference on Systems, Man,
and Cybernetics (SMC)",
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title = "A Comparative Analysis of Dimensionality Reduction
Methods for Genetic Programming to Solve
High-Dimensional Symbolic Regression Problems",
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year = "2021",
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pages = "476--483",
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abstract = "Genetic Programming (GP) is a powerful evolutionary
algorithm that has a wide range of real-world
applications. High-dimensional symbolic regression
(HDSR) is an important yet challenging application of
GP. In this paper, a comparative study is conducted to
investigate and to discuss the effectiveness of
dimensionality reduction (DR) techniques in assisting
GP for HDSR problems. Three popular DR techniques,
which are the Pearson Correlation Coefficient (PCC),
the Principal Component Analysis (PCA), and the Maximal
Information Coefficient (MIC), are selected for
comparison and discussion. The experimental results
showed that considering only correlation during DR is
not effective enough to provide a suitable reduced set
of problem dimensions, and that GP with DR may perform
worse than its counterpart without DR. Meanwhile, we
propose a novel two-phase DR method, considering both
correlation and redundancy. The proposed method can
give a more reasonable set of reduced dimensions, which
can effectively improve the performance of GP on HDSR
problems.",
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keywords = "genetic algorithms, genetic programming,
Dimensionality reduction, Microwave integrated
circuits, Correlation, Redundancy, Evolutionary
computation, Feature extraction",
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DOI = "doi:10.1109/SMC52423.2021.9658595",
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ISSN = "2577-1655",
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month = oct,
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notes = "Also known as \cite{9658595}",
- }
Genetic Programming entries for
Lianjie Zhong
Jinghui Zhong
Chengyu Lu
Citations