Algorithmic Prompt-Augmentation for Efficient LLM-Based Heuristic Design for A* Search
This addresses the need for efficient heuristic design in search algorithms, reducing reliance on expert knowledge, though it is incremental as it builds on existing evolutionary frameworks.
The paper tackles the problem of automating heuristic design for A* search by extending the Evolution of Heuristics framework with a domain-agnostic prompt augmentation strategy, resulting in generated heuristics that significantly improve quality and outperform expert-designed ones in computational experiments on two problem domains.
Heuristic functions are essential to the performance of tree search algorithms such as A*, where their accuracy and efficiency directly impact search outcomes. Traditionally, such heuristics are handcrafted, requiring significant expertise. Recent advances in large language models (LLMs) and evolutionary frameworks have opened the door to automating heuristic design. In this paper, we extend the Evolution of Heuristics (EoH) framework to investigate the automated generation of guiding heuristics for A* search. We introduce a novel domain-agnostic prompt augmentation strategy that includes the A* code into the prompt to leverage in-context learning, named Algorithmic - Contextual EoH (A-CEoH). To evaluate the effectiveness of A-CeoH, we study two problem domains: the Unit-Load Pre-Marshalling Problem (UPMP), a niche problem from warehouse logistics, and the classical sliding puzzle problem (SPP). Our computational experiments show that A-CEoH can significantly improve the quality of the generated heuristics and even outperform expert-designed heuristics.