MetaSR: A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@InProceedings{fong:2024:GECCO,

• author = "Kei Sen Fong and Mehul Motani",
• title = "{MetaSR:} A Meta-Learning Approach to Fitness Formulation for Frequency-Aware Symbolic Regression",
• booktitle = "Proceedings of the 2024 Genetic and Evolutionary Computation Conference",
• year = "2024",
• editor = "Ting Hu and Aniko Ekart and Julia Handl and Xiaodong Li and Markus Wagner and Mario Garza-Fabre and Kate Smith-Miles and Richard Allmendinger and Ying Bi and Grant Dick and Amir H Gandomi and Marcella Scoczynski Ribeiro Martins and Hirad Assimi and Nadarajen Veerapen and Yuan Sun and Mario Andres Munyoz and Ahmed Kheiri and Nguyen Su and Dhananjay Thiruvady and Andy Song and Frank Neumann and Carla Silva",
• pages = "878--886",
• 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, symbolic regression, meta-learning",
• isbn13 = "979-8-4007-0494-9",
• DOI = "doi:10.1145/3638529.3654096",
• size = "9 pages",
• abstract = "State-of-the-art Symbolic Regression (SR) algorithms employ evolutionary techniques to fulfill the task of generating a concise mathematical expression that fulfills an objective. A common objective is to fit to a dataset of input-output pairs, in which the faithfulness of a predicted output to the actual output is used as the fitness measure (e.g., R-squared). In many datasets, among the candidate expressions evaluated, there tends to be a large number of pseudo-expressions, referring to expressions that achieve high fitness but do not resemble the ground-truth equation. These pseudo-expressions decrease the equation recovery rate of SR algorithms. To formulate novel fitness measures that function as better discriminators of the ground-truth equation, we introduce a novel meta-learning approach to SR, MetaSR, in which we use SR itself to discover new fitness measures that can be complex combinations of existing base measures. In this paper, we focus on frequency-aware symbolic regression, where the fitness can depend on the frequency domain. We show that our new fitness measures better discriminate the ground-truth equation from other equations and demonstrate the improved performance of our method against existing algorithms.",
• 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 Kei Sen Fong Mehul Motani

Citations