AlgoEvo: Self-Evolving Agentic Search for Automated Algorithm Discovery
[Sztuczna inteligencja] Autorzy: Junhao Qiu, Qinglong Hu, Xialiang Tong i 3 więcej
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-ta…
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja