- author = "Chao Xu and Junlan Dong and Yanqiao Lv and Jinghui Zhong",
- title = "Transformer-Guided Genetic Programming for Symbolic Regression",
- booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
- year = "2025",
- editor = "Aniko Ekart and Nelishia Pillay",
- pages = "663--666",
- 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, transformer, symbolic regression: Poster",
- isbn13 = "979-8-4007-1464-1",
-
URL = "
https://doi.org/10.1145/3712255.3726761",
-
DOI = "
10.1145/3712255.3726761",
- size = "4 pages",
- abstract = "Genetic programming (GP) algorithms are widely used for solving symbolic regression (SR) problems. However, traditional GP struggles to effectively leverage knowledge gained from previous tasks when tackling new ones, leading to inefficient search processes. At the same time, the Transformer architecture has demonstrated superior performance through extensive experimental validation. To address this issue, we propose a novel approach called Transformer-GP. The core idea behind this method is to train a Transformer-Tree model to learn the distribution characteristics of node and edge components in symbolic trees. The model's predicted outputs are subsequently used as prior knowledge to guide genetic operations, including initialization and mutation, within the GP population. We conducted experiments on 12 benchmark problems, and the results demonstrate the effectiveness of the proposed method.",
-
notes = "GECCO-2025 GP
A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",
