Post-Hoc Refinement for Multitask Symbolic Regression via Consensus-Accelerated Shapley Analysis

Abstract

Multitask genetic programming (MTGP) is one of the primary methods for solving multitask symbolic regression (MTSR), the problem of discovering mathematical expressions for multiple interconnected tasks simultaneously. However, conventional MTGP approaches discard a wealth of valuable knowledge from the population of expressions due to their inherent “winner-take-all” selection criteria. To address this, we introduce MTGP with bidirectional cooperation and consensus-accelerated Shapley analysis (MTGP-BS), a method whose core is a novel post-hoc refinement framework that shifts from selection to synthesis. Our method first employs a consensus-accelerated Shapley analysis to reliably identify important subexpressions by multi-model attribution. Second, to supply this analysis with high-quality candidates, we design a bidirectional subexpression cooperative extraction method to create a refined archive of effective components by improving knowledge transfer and filtering out redundancies. These allow MTGP-BS to synthesize superior expressions by integrating knowledge dispersed throughout the entire population. On diverse MTSR problems, our algorithm statistically outperformed state-of-the-art approaches in 140 out of 160 direct comparisons, with its effectiveness and practical utility further verified by real-world case studies and in-depth ablation analyses.