MMSR: Symbolic regression is a multi-modal information fusion task

Created by W.Langdon from gp-bibliography.bib Revision:1.8414

@Article{Li:2025:inffus,

• author = "Yanjie Li and Jingyi Liu and Min Wu and Lina Yu and Weijun Li and Xin Ning and Wenqiang Li and Meilan Hao and Yusong Deng and Shu Wei",
• title = "{MMSR:} Symbolic regression is a multi-modal information fusion task",
• journal = "Information Fusion",
• year = "2025",
• volume = "114",
• pages = "102681",
• keywords = "genetic algorithms, genetic programming, Symbolic regression, Multi-modal, Information fusion, Contrastive learning, Modal alignment",
• ISSN = "1566-2535",
• URL = "https://www.sciencedirect.com/science/article/pii/S1566253524004597",
• DOI = "doi:10.1016/j.inffus.2024.102681",
• abstract = "Mathematical formulas are the crystallization of human wisdom in exploring the laws of nature for thousands of years. Describing the complex laws of nature with a concise mathematical formula is a constant pursuit of scientists and a great challenge for artificial intelligence. This field is called symbolic regression (SR). Symbolic regression was originally formulated as a combinatorial optimisation problem, and Genetic Programming (GP) and Reinforcement Learning algorithms were used to solve it. However, GP is sensitive to hyperparameters, and these two types of algorithms are inefficient. To solve this problem, researchers treat the mapping from data to expressions as a translation problem. And the corresponding large-scale pre-trained model is introduced. However, the data and expression skeletons do not have very clear word correspondences as the two languages do. Instead, they are more like two modalities (e.g., image and text). Therefore, in this paper, we proposed MMSR. The SR problem is solved as a pure multi-modal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion. It is worth noting that to better promote the modal feature fusion, we adopt the strategy of training contrastive learning loss and other losses at the same time, which only needs one-step training, instead of training contrastive learning loss first and then training other losses. Because our experiments prove training together can make the feature extraction module and feature fusion module wearing-in better. Experimental results show that compared with multiple large-scale pre-training baselines, MMSR achieves the most advanced results on multiple mainstream datasets including SRBench. Our code is open source at https://github.com/1716757342/MMSR",

}

Genetic Programming entries for Yanjie Li Jingyi Liu Min Wu Lina Yu Weijun Li Xin Ning Wenqiang Li Meilan Hao Yusong Deng Shu Wei

Citations