Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{zhang:2023:PRICAI,
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author = "Hengzhe Zhang and Qi Chen and Bing Xue and
Wolfgang Banzhaf and Mengjie Zhang",
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title = "Automatically Choosing Selection Operator Based on
Semantic Information in Evolutionary Feature
Construction",
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booktitle = "Pacific Rim International Conference on Artificial
Intelligence",
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year = "2023",
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editor = "Fenrong Liu and Arun Anand Sadanandan and
Duc Nghia Pham and Petrus Mursanto and Dickson Lukose",
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volume = "14326",
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series = "Lecture Notes in Computer Science",
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pages = "385--397",
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address = "Jakarta, Indonesia",
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month = nov # " 17-19",
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publisher = "Springer Nature",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Feature Construction, Adaptive Operator Selection",
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isbn13 = "978-981-99-7022-3",
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DOI = "doi:10.1007/978-981-99-7022-3_36",
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abstract = "In recent years, genetic programming-based
evolutionary feature construction has shown great
potential in various applications. However, a critical
challenge in applying this technique is the need to
select an appropriate selection operator with great
care. To tackle this issue, this paper introduces a
novel approach that leverages the Thompson sampling
technique to automatically choose the optimal selection
operator based on semantic information of genetic
programming models gathered during the evolutionary
process. The experimental results on a standard
symbolic regression benchmark containing 37 datasets
show that the proposed adaptive operator selection
algorithm outperforms expert-designed operators,
demonstrating the effectiveness of the adaptive
operator selection algorithm.",
-
notes = "https://www.pricai.org/2023/",
- }
Genetic Programming entries for
Hengzhe Zhang
Qi Chen
Bing Xue
Wolfgang Banzhaf
Mengjie Zhang
Citations