SPINEX_ Symbolic Regression: Similarity-based Symbolic Regression with Explainable Neighbors Exploration
This work addresses symbolic regression for researchers and practitioners in fields like physics and data science, but it appears incremental as it builds on the existing SPINEX family with a new application.
The authors tackled the problem of symbolic regression by introducing SPINEX_SymbolicRegression, a similarity-based algorithm that identifies high-merit expressions using accuracy and structural similarity metrics, and it consistently performs well, sometimes outperforming leading algorithms in benchmarking tests on over 180 functions.
This article introduces a new symbolic regression algorithm based on the SPINEX (Similarity-based Predictions with Explainable Neighbors Exploration) family. This new algorithm (SPINEX_SymbolicRegression) adopts a similarity-based approach to identifying high-merit expressions that satisfy accuracy- and structural similarity metrics. We conducted extensive benchmarking tests comparing SPINEX_SymbolicRegression to over 180 mathematical benchmarking functions from international problem sets that span randomly generated expressions and those based on real physical phenomena. Then, we evaluated the performance of the proposed algorithm in terms of accuracy, expression similarity in terms of presence operators and variables (as compared to the actual expressions), population size, and number of generations at convergence. The results indicate that SPINEX_SymbolicRegression consistently performs well and can, in some instances, outperform leading algorithms. In addition, the algorithm's explainability capabilities are highlighted through in-depth experiments.