Genetic Programming-Based Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]

Created by W.Langdon from gp-bibliography.bib Revision:1.7810

@InProceedings{zhang:2023:GECCOcompA,

• author = "Hengzhe Zhang and Aimin Zhou and Qi Chen and Bing Xue and Mengjie Zhang",
• title = "Genetic {Programming-Based} Evolutionary Feature Construction for Heterogeneous Ensemble Learning [Hot of the Press]",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Alberto Moraglio",
• pages = "49--50",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary feature construction, heterogeneous ensemble learning",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3595831",
• size = "2 pages",
• abstract = "This Hof-off-the-Press paper summarizes our recently published work, {"}SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method,{"} published in IEEE Transactions on Evolutionary Computation [4]. This paper presents SR-Forest, a novel genetic programming-based heterogeneous ensemble learning method, which combines the strengths of decision trees and genetic programming-based symbolic regression methods. Rather than treating genetic programming-based symbolic regression methods as competitors to random forests, we propose to enhance the performance of random forests by incorporating genetic programming as a complementary technique. We introduce a guided mutation operator, a multi-fidelity evaluation strategy, and an ensemble selection mechanism to accelerate the search process, reduce computational costs, and improve predictive performance. Experimental results on a regression benchmark with 120 datasets show that SR-Forest outperforms 25 existing symbolic regression and ensemble learning methods. Moreover, we demonstrate the effectiveness of SR-Forest on an XGBoost hyperparameter performance prediction task, which is an important application area of ensemble learning methods. Overall, SR-Forest provides a promising approach to solving regression problems and can serve as a valuable tool in real-world applications.",
• notes = "Also known as \cite{zhang:2023:GECCOcomp}. GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Hengzhe Zhang Aimin Zhou Qi Chen Bing Xue Mengjie Zhang

Citations