Controlled Self-Evolution for Algorithmic Code Optimization

Understand

Self-evolution methods enhance code generation through iterative "generate-verify-refine" cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets.

• This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, and insufficient experience utilization across tasks.
• To address these bottlenecks, we propose Controlled Self-Evolution (CSE), which consists of three key components.
• Diversified Planning Initialization generates structurally distinct algorithmic strategies for broad solution space coverage.