Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{zhang:2023:PRICAI,

• author = "Hengzhe Zhang and Qi Chen and Bing Xue and Wolfgang Banzhaf and Mengjie Zhang",
• title = "Automatically Choosing Selection Operator Based on Semantic Information in Evolutionary Feature Construction",
• booktitle = "Pacific Rim International Conference on Artificial Intelligence",
• year = "2023",
• editor = "Fenrong Liu and Arun Anand Sadanandan and Duc Nghia Pham and Petrus Mursanto and Dickson Lukose",
• volume = "14326",
• series = "Lecture Notes in Computer Science",
• pages = "385--397",
• address = "Jakarta, Indonesia",
• month = nov # " 17-19",
• publisher = "Springer Nature",
• keywords = "genetic algorithms, genetic programming, Evolutionary Feature Construction, Adaptive Operator Selection",
• isbn13 = "978-981-99-7022-3",
• DOI = "doi:10.1007/978-981-99-7022-3_36",
• 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