Novel Application of Mutual Information in Transfer Learning for Genetic Programming

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

@InProceedings{liu:2025:GECCOcomp3,

• author = "Yilin Liu and Gareth Taylor and Zhengwen Huang",
• title = "Novel Application of Mutual Information in Transfer Learning for Genetic Programming",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Aniko Ekart and Nelishia Pillay",
• pages = "635--638",
• 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, transfer learning, mutual information: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726607",
• DOI = "doi:10.1145/3712255.3726607",
• size = "4 pages",
• abstract = "Genetic Programming (GP) faces two major challenges: the inability to use knowledge from past problems, as each run is independent, and the difficulty in identifying building blocks in the early stages of evolution. These limitations result in high computational costs and slower convergence. Transfer learning can provide a solution by leveraging past experiences to enhance performance in GP. A mutual-information-based transfer learning method is proposed in this paper to identify and transfer beneficial knowledge fragments. The method is evaluated on ten polynomial and trigonometric symbolic regression problems from previous literature and compared with standard GP and SubTree50 as state-of-the-art methods. Results of the above experiment demonstrate improved or comparable performance of the proposed method in terms of accuracy, statistical significance, and generalization. The contribution of this paper includes: a mutual-information-based technique for identifying and transferring knowledge fragments. Results highlight the potential of mutual information to address key challenges in GP effectively.",
• notes = "GECCO-2025 GP A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Yilin Liu Gareth A Taylor Zhengwen Huang

Citations