RL-GEP: Symbolic Regression via Gene Expression Programming and Reinforcement Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{conf/ijcnn/ZhangZ21a,
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author = "Hengzhe Zhang and Aimin Zhou",
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title = "{RL-GEP}: Symbolic Regression via Gene Expression
Programming and Reinforcement Learning",
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booktitle = "2021 International Joint Conference on Neural
Networks, IJCNN",
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year = "2021",
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address = "Shenzhen, China",
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month = "18-22 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, reinforcement learning,
symbolic regression",
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isbn13 = "978-1-6654-3900-8",
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bibdate = "2021-09-29",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2021.html#ZhangZ21a",
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URL = "https://ieeexplore.ieee.org/document/9533735",
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DOI = "doi:10.1109/IJCNN52387.2021.9533735",
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size = "8 pages",
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abstract = "Symbolic regression has become a hot topic in recent
years due to the surging demand for interpretable
machine learning methods. Traditionally, symbolic
regression problems are mainly solved by genetic
algorithms. Nonetheless, with the development of deep
learning, reinforcement learning based symbolic
regression methods have received attention gradually.
Unfortunately, hardly any of those reinforcement
learning based methods have been proven effectively to
solve real world regression problems as genetic
algorithm based methods. In this paper, we find a
general reinforcement learning based symbolic
regression method is difficult to solve real world
problems since it is hard to balance between
exploration and exploitation. To deal with this
problem, we propose a hybrid method to use both genetic
algorithm and reinforcement learning for solving
symbolic regression problems. By doing so, we can
combine the advantages of reinforcement learning and
genetic algorithm and achieve better performance than
using them alone. To validate the effectiveness of the
proposed method, we apply the proposed method to ten
benchmark datasets. The experimental results show that
the proposed method achieves competitive performance
compared with several well-known symbolic regression
methods on those datasets.",
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notes = "Institute of AI Education, School of Computer Science
and Technology East China Normal University, Shanghai
200062, China",
- }
Genetic Programming entries for
Hengzhe Zhang
Aimin Zhou
Citations