Automated Heuristic Design

Automated heuristic design studies how to create efficient rules that guide search and decision-making in problems such as vehicle routing, job scheduling, classical planning, and hardware design. Common approaches use genetic programming, evolutionary search, learned policies, or large language models to generate and refine executable heuristics using solver or simulator feedback. Current work increasingly combines these methods with memory, diverse heuristic portfolios, instance specialization, and transfer across tasks, while evaluating solution quality, runtime, and performance on held-out cases. A central challenge is balancing flexible search with reliable generalization, affordable evaluation, and guarantees such as execution safety or admissibility.