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Life sciences · Preprint
arXiv · August 14, 2026
Early or partial results. Treat as a signal, not a conclusion.
This preprint presents an adaptive protection mechanism for evolutionary feature construction in symbolic regression, designed to preserve important constructed features during genetic algorithm evolution. Computational benchmarking on 98 regression datasets and 2 credit classification datasets shows claimed improvements over baseline approaches, but the work lacks peer review, clinical validation, and rigorous statistical comparison.
Computational algorithm benchmarking study. 98 regression benchmark datasets and 2 credit classification datasets. Intervention: Adaptive protection mechanism leveraging feature importance metrics to preserve constructed features during evolutionary symbolic regression. Compared with: Baseline approaches (unspecified in abstract).
Adaptive protection mechanism improves solution quality across 98 regression benchmark datasets compared to baseline approaches Method extends to credit classification tasks beyond symbolic regression Robustness demonstrated across different feature importance calculation methods and base learners
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This is an algorithmic development paper demonstrating a computational method on benchmark datasets without clinical validation, peer review, or comparison to established standards in a clinical domain.
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Evolutionary feature construction has shown strong promise in symbolic regression by automatically discovering informative transformations of input features that enhance a simple base learner. However, existing approaches often lack explicit mechanisms to preserve important constructed features discovered during evolution, and valuable genetic material can be lost when genetic operators disrupt effective features. This paper introduces an adaptive protection mechanism that leverages feature importance metrics to selectively preserve constructed features during evolution. The mechanism provides stronger protection for more important constructed features while still allowing less important features to be modified and to incorporate useful building blocks from more important features. We evaluate the approach using multiple feature importance calculation methods and demonstrate its robustness across different base learners. Experimental results on 98 regression benchmark datasets show that the proposed mechanism consistently improves solution quality over baseline approaches, and experiments on two credit classification datasets demonstrate that the method also extends effectively to improve search effectiveness beyond symbolic regression.
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