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Controlled Self-Evolution For Algorithmic Code Optimization by blacknickwield is a document available to read on EtoBox.

What is Controlled Self-Evolution For Algorithmic Code Optimization about?

The document presents Controlled Self-Evolution (CSE), a novel framework for optimizing algorithmic code generation through improved exploration efficiency. CSE addresses limitations of existing self-evolution methods by incorporating diversified planning initialization, feedback-guided genetic evolution, and hierarchical evolution memory. Experiments demonstrate that CSE consistently outperforms baseline methods across various LLM backbones, achieving higher efficiency and continuous improvement throughout

Author

blacknickwield

Language

EN