Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability

Created by W.Langdon from gp-bibliography.bib Revision:1.8344

@InProceedings{fong:2023:GECCOcomp,

• author = "Kei Sen Fong and Shelvia Wongso and Mehul Motani",
• title = "Evolutionary Symbolic Regression: Mechanisms from the Perspectives of Morphology and Adaptability",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Alberto Moraglio",
• pages = "21--22",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, symbolic regression",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3595830",
• size = "2 pages",
• abstract = "Symbolic Regression (SR) is the task of finding closed-form analytical expressions that describe the relationship between variables in a dataset. In this work, werethink SR and introduce mechanisms from two perspectives: morphology and adaptability. Morphology: Man-made heuristics are typically used in SR algorithms to influence the morphology (or structure) of candidate expressions, potentially introducing unintentional bias and data leakage. To address this issue, we create a depth-aware mathematical language model trained on terminal walks of expression trees, as a replacement to these heuristics. Adaptability: We promote alternating fitness functions across generations, eliminating equations that perform well in only one fitness function and as a result, discover expressions that are closer to the true functional form. We demonstrate this by alternating fitness functions that quantify faithfulness to values (via MSE) and empirical derivatives (via a novel theoretically justified fitness metric coined MSEDI). Proof-of-concept: We combine these ideas into a minimalistic evolutionary SR algorithm that outperforms a suite of benchmark and state of-the-art SR algorithms in problems with unknown constants added, which we claim are more reflective of SR performance for real-world applications. Our claim is then strengthened by reproducing the superior performance on real-world regression datasets from SRBench. This Hot-of-the-Press paper summarizes the work K.S. Fong, S. Wongso and M. Motani, {"}Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms{"}, The Eleventh International Conference on Learning International Conference on Learning Representations (ICLR'23).",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Kei Sen Fong Shelvia Wongso Mehul Motani

Citations