Adaptive Protection for Evolutionary Feature Construction in Symbolic Regression with Application to Credit Classification

2026-08-14

AI Summary

This study addresses the loss of critical features in evolutionary feature construction for symbolic regression due to the absence of protective mechanisms. To overcome this, we propose a feature importance-driven adaptive protection strategy that introduces dynamic differential protection into the evolutionary process for the first time. By synergistically integrating multiple importance metrics with genetic programming, this approach preserves high-value features while permitting inefficient ones to evolve and absorb effective building blocks. Extensive experiments across 98 regression benchmarks and two classification datasets demonstrate that the proposed mechanism significantly enhances search efficiency, solution quality, and model generalization. These results validate the robustness of the method in handling complex tasks, offering a principled solution to feature preservation in evolutionary computation.

Abstract

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.

Problem

• Symbolic Regression
• Evolutionary Feature Construction
• Feature Preservation
• Genetic Operators

Innovation

• Adaptive Protection
• Evolutionary Feature Construction
• Symbolic Regression
• Feature Importance
• Genetic Operators