CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models
With the rapid advancement of large language models (LLMs), LLM-based heuristic search methods have demonstrated strong capabilities in automated algorithm generation. However, their evolutionary processes often suffer from instability and premature convergence. Existing approaches mainly address this issue through prompt engineering or by jointly evolving thought and code, while largely overlooking the critical role of algorithmic category diversity in maintaining evolutionary stability. To this end, we propose Category Driven Automatic Algorithm Design with Large Language Models (CDEoH), which explicitly models algorithm categories and jointly balances performance and category diversity...
| Authors | Yu-Nian Wang, Shen-Huan Lyu, Ning Chen et al. |
|---|---|
| Primary category | cs.NE |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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