ConfigAgent: Agentic Algorithm Configuration for Combinatorial Optimization

Algorithm configuration (AC) is essential in combinatorial optimization (CO), yet existing methods such as SMAC3 and irace remain context-specific, and need to repeatedly tune for each solver-problem pair from scratch, limiting transferability and efficiency. We propose ConfigAgent, an agentic framework that reformulates AC as knowledge-driven reasoning over accumulated configuration experience across multiple contexts. It maintains a structured knowledge base containing historical trials and distilled insights across different solvers, problem families, objectives, and instance scales. For a new configuration context at deployment, ConfigAgent retrieves relevant prior experience from the knowledge base, reasons over contextual similarities, and generates evidence-grounded configurations. We evaluate ConfigAgent on five CO families with a wide range of algorithms including exact solvers, heuristic search engines, and multi-objective evolutionary algorithms. Results show that ConfigAgent consistently matches or outperforms strong AC baselines while requiring substantially fewer configuration trials. Moreover, the learned knowledge base enables efficient zero-shot or few-shot transfer to unseen scales, objectives, and structural variants, significantly reducing deployment cost by up to two orders of magnitude.