Archive-based multiple feature construction methods using adaptive Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8187

@InProceedings{jia:2024:GECCOcomp2,

• author = "Kaixuan Jia and Jianbin Ma and Xiaoying Gao and Jiaxin Niu",
• title = "Archive-based multiple feature construction methods using adaptive Genetic Programming",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart",
• pages = "503--506",
• address = "Melbourne, Australia",
• series = "GECCO '24",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, feature construction, adaptive, multiple feature, classification: Poster",
• isbn13 = "979-8-4007-0495-6",
• DOI = "doi:10.1145/3638530.3654239",
• size = "4 pages",
• abstract = "The quality of features is an important factor that affects the classification performance of machine learning algorithms. Feature construction based on Genetic Programming (GP) can automatically create more discriminative features, sometimes greatly improving classification performance. However, insufficient information caused by constructing a single feature or constructing only a few features can affect the classification performance of feature construction. In addition, GP may fall into premature convergence, which also affects classification performance. This paper proposes an archive-based multiple feature construction method which uses elite archive strategy to preserve and select effective constructed features, and employs an adaptive strategy for GP to adjust the crossover and mutation probabilities based on fitness values. Experiments on ten datasets show that our proposed archive-based multiple feature construction method without using adaptive GP can significantly improve the classification performance compared with traditional single feature construction method, and the classification performance can be maintained or further improved by adding the adaptive strategy. The comparisons with three state-of-the-art techniques show that our proposed methods can significantly achieve better classification performance.",
• notes = "GECCO-2024 GP A Recombination of the 33rd International Conference on Genetic Algorithms (ICGA) and the 29th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Kaixuan Jia Jianbin Ma Xiaoying (Sharon) Gao Jiaxin Niu

Citations