Accelerating LLM-Based Algorithm Evolution for the 3D Container Loading Problem
Designing effective heuristics remains a labor-intensive task traditionally reserved for domain experts. While Large Language Model (LLM)-driven evolutionary search offers a path toward automated discovery, existing methods often suffer from slow convergence, primarily due to inefficient hyperparameter tuning. Delegating tuning to specialized optimizers improves performance, but it comes at the cost of code bloat and overfitting. To address these issues, we propose a pipeline that introduces a novel regularization architecture balancing performance and complexity. Specifically, we mitigate the side effects of automated tuning through two novel components: (i) symbolic pruning mutator, which combines LLM semantic guidance with Abstract Syntax Tree analysis to eliminate algorithmic redundancy; and (ii) a complexity-aware mutation gate that explicitly filters out mutations leading to excessive code growth. Our framework substantially accelerates convergence and improves generalization on standard benchmarks of the 3D Single Container Loading Problem. The discovered heuristics match state-of-the-art human designed algorithms and rediscover similar geometric principles used by experts, highlighting the framework's ability to autonomously extract meaningful domain knowledge.