CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models
📰 ArXiv cs.AI
CDEoH uses large language models for automatic algorithm design, addressing instability and premature convergence in evolutionary processes
Action Steps
- Identify the algorithmic category to drive the search process
- Utilize large language models to generate algorithms based on the category
- Evaluate and refine the generated algorithms to ensure stability and convergence
- Apply the refined algorithms to solve complex problems
Who Needs to Know This
ML researchers and AI engineers can benefit from CDEoH as it improves the efficiency and effectiveness of automated algorithm generation, while software engineers can apply the generated algorithms to various problems
Key Insight
💡 Category-driven approach can improve the stability and convergence of LLM-based algorithm generation
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💡 CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models
Key Takeaways
CDEoH uses large language models for automatic algorithm design, addressing instability and premature convergence in evolutionary processes
Full Article
Title: CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models
Abstract:
arXiv:2603.19284v1 Announce Type: cross Abstract: 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 diver
Abstract:
arXiv:2603.19284v1 Announce Type: cross Abstract: 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 diver
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