Large Language Model Driven Operator Evolution: A Paradigm for Operator Pool Reconstruction and Adaptation

Evolutionary search performance strongly depends on operator design, while most existing methods remain limited to predefined operators or search behaviors. This paper proposes a feedback-driven operator evolution framework that uses large language models (LLMs) to reconstruct search behaviors within a bounded parameter space. Candidate operators combine random exploration, constraint improvement, objective convergence, and diversity restoration, while offspring generation retains the original GA/DE variation interfaces. Candidate operators are evaluated according to their search performance, which provides feedback for LLM-based reconstruction. Reconstructed candidates are evaluated again before participating in main evolution, while the original operator remains available throughout the search. The framework is integrated into representative constrained multiobjective evolutionary algorithms. Experiments are conducted on the LIR-CMOP and DAS-CMOP benchmarks, together with the real-world problem sets RWMOP and MOOPF. Experimental results show improved overall performance across the tested problems.