AlgoEvo框架实现自动化算法发现自我进化
AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
这个框架挺有意思，它让算法发现自己进化，比传统方法更高效，值得看看。
AlgoEvo是一个统一的框架，它将自动化算法发现变成一个交互式、积累知识的过程。一个自主代理根据运行时反馈动态检查、诊断和编辑代码。在六个代表性基准任务上，AlgoEvo在减少评估次数和减少token消耗的同时，匹配或超越了专用方法，展示了强大的任务内积累、跨任务迁移和通过灵活技能激活重现或超越现有SOTA性能的能力。
AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
Large language models have advanced automated algorithm discovery by synthesizing executable code, but existing frameworks trap them in rigid search pipelines with pre-defined control flows. This limitation restricts adaptive reasoning, blocks cross-paradigm transfer, and discards valuable execution feedback. We propose AlgoEvo, a unified agentic framework that transforms automated algorithm discovery into an interactive, knowledge-accumulating process. An autonomous agent dynamically inspects, diagnoses, and edits code based on runtime feedback. A design skill hub decouples paradigm-specific knowledge from the core discovery engine, allowing a single workflow to seamlessly handle single-objective, multi-objective, and multi-component design. Meanwhile, a hierarchical experience mechanism organizes search trajectories into a task-level tree to guide exploration and consolidates cross-task patterns into reusable skills. Across six representative benchmark tasks, AlgoEvo matches or surpasses specialized methods with substantially fewer evaluations and reduced token consumption, demonstrating strong intra-task accumulation, cross-task transfer, and the ability to reproduce or exceed existing state-of-the-art performance through flexible skill activation.
