Authors
Kirill Antonov, Roman Kalkreuth, Kaifeng Yang, Thomas Bäck, Niki Stein, Anna Kononova
Publication date
2024/7/14
Book
Proceedings of the Genetic and Evolutionary Computation Conference
Pages
859-867
Description
Symbolic regression (SR) poses a significant challenge for randomized search heuristics due to its reliance on the synthesis of expressions for input-output mappings. Although traditional genetic programming (GP) algorithms have achieved success in various domains, they exhibit limited performance when tree-based representations are used for SR. To address these limitations, we introduce a novel SR approach called Fourier Tree Growing (FTG) that draws insights from functional analysis. This new perspective enables us to perform optimization directly in a different space, thus avoiding intricate symbolic expressions. Our proposed algorithm exhibits significant performance improvements over traditional GP methods on a range of classical one-dimensional benchmarking problems. To identify and explain the limiting factors of GP and FTG, we perform experiments on a large-scale polynomials benchmark with …
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K Antonov, R Kalkreuth, K Yang, T Bäck, N Stein… - Proceedings of the Genetic and Evolutionary …, 2024