Controlled Self-Evolution for Algorithmic Code Optimization
This work proposes a controllable self-evolution framework to address the limitations of existing methods, which under limited computational budgets suffer from initialization bias, undirected random operations without feedback, and insufficient exploitation of cross-task experience, thereby hindering efficient discovery of superior algorithmic code. The proposed approach integrates structurally diverse planning-based initialization, feedback-guided directional mutation and crossover, and a hierarchical evolutionary memory that synergistically combines both intra-task and cross-task experiences. Evaluated on the EffiBench-X benchmark, the method significantly outperforms current state-of-the-art techniques, demonstrates compatibility with multiple large language models, achieves high efficiency early in the evolution process, and consistently improves code performance over time.