Budget-Efficient Automatic Algorithm Design via Code Graph
Researchers have developed a new framework for automatic algorithm design (AAD) that leverages large language models (LLMs) more efficiently. Instead of generating entire algorithms, the system uses LLMs to produce compact code block corrections that augment a directed acyclic graph representation of algorithms. This approach allows for more granular credit assignment and better exploitation of algorithmic features, outperforming traditional full-algorithm search methods within the same computational budget. AI
IMPACT Introduces a more efficient method for using LLMs in algorithm design, potentially accelerating the development of optimization solutions.