通过代码演化学习求解与优化
Learning to Solve and Optimize by Evolving Code
AI总结：
提出CHECKMATE工具，利用形式规范确保解的正确性并通过自然语言描述指导代码演化，自动生成算法，在配置与调度问题上超越最先进求解器。
摘要：
组合与优化问题是许多工业AI应用的基础。解决此类大规模现实世界实例通常需要仔细的问题形式化、专门的求解器以及专家设计的启发式方法。因此，专家不仅需要指定解是什么，还需要指定如何推导出解。通过引入工具CHECKMATE，我们展示了通过代码演化生成算法代表了一种范式转变，消除了制定如何的需求。CHECKMATE仅依赖于是什么。具体来说，形式规范确保了解的正确性，并能够对生成的程序进行系统性能评估，而自然语言描述则指导演化过程。我们的方法在两个工业领域（配置与调度）的选定问题上展示了有效性。在所有案例中，演化出的算法始终优于最先进的求解器。这凸显了形式方法在引导代码演化以自动解决复杂现实问题方面的潜力。
英文摘要：
Combinatorial and optimization problems are fundamental to many industrial AI applications. Solving large-scale real-world instances of such problems typically requires careful problem formalization, specialized solvers, and expert-designed heuristics. Thus, experts need to specify not only what solutions are, but also how they are derived. By introducing the tool CHECKMATE, we show that algorithm generation via code evolution represents a paradigm shift by eliminating the need to formulate the how. CHECKMATE solely relies on the what. Specifically, a formal specification ensures solutions' correctness and enables systematic performance evaluation of the generated programs, while a natural language description guides the evolutionary process. The effectiveness of our method is demonstrated on selected problems from two industrial domains: configuration and scheduling. In all cases, the evolved algorithms consistently outperform state-of-the-art solvers. This underscores the potential of formal methods in guiding code evolution for automatically solving complex real-world problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
