Genetic Programming Based Feature Construction for Automated Algorithm Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.8528

@InProceedings{guo:2025:GECCOcomp,

• author = "Qingbin Guo and Handing Wang",
• title = "Genetic Programming Based Feature Construction for Automated Algorithm Selection",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Marie-Eleonore Kessaci and Anna V. Kononova",
• pages = "683--686",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, feature construction, automated algorithm selection, exploratory landscape analysis, Learning for Evolutionary Computation: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726557",
• DOI = "doi:10.1145/3712255.3726557",
• size = "4 pages",
• abstract = "Automated algorithm selection aims to help users to select the best algorithm for new optimization problems without any expertise. As a machine learning task, algorithm selection maps the problem features to the best algorithm. Exploratory landscape analysis is a feature extraction method for algorithm selection based on random samples, which has been widely applied in algorithm selection methods. However, their instability and redundancy greatly affect the accuracy of algorithm selection. To enhance these features, we apply a feature construction method based on multi-tree genetic programming for algorithm selection, which creates more discriminating features by combining or transforming multiple features and further counteracts the negative correlation noise from random samples. The selection process in genetic programming can reduce the redundancy of features by selecting more relevant features. Experimental results show that this method achieves the best performance compared with genetic programming based feature construction methods and algorithm selection methods on the four datasets.",
• notes = "GECCO-2025 L4EC A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Qingbin Guo Handing Wang

Citations