Controlled Self-Evolution for Algorithmic Code Optimization

This paper proposes Controlled Self-Evolution (CSE) to enhance code generation through iterative generate-verify-refine cycles. It addresses inefficiencies in existing self-evolution methods for algorithmic code optimization by improving exploration efficiency and experience utilization, leading to more efficient and algorithmically optimal solutions.