CDEoH: Category-Driven Automatic Algorithm Design With Large Language Models
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 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 in population management, enabling parallel exploration across multiple algorithmic paradigms. Extensive experiments on representative combinatorial optimization problems across multiple scales demonstrate that CDEoH effectively mitigates convergence
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 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 in population management, enabling parallel exploration across multiple algorithmic paradigms. Extensive experiments on representative combinatorial optimization problems across multiple scales demonstrate that CDEoH effectively mitigates convergence toward a single evolutionary direction, significantly enhancing evolutionary stability and achieving consistently superior average performance across tasks and scales.
Executive Summary
This article introduces Category Driven Automatic Algorithm Design with Large Language Models (CDEoH), a novel approach to addressing the instability and premature convergence issues associated with large language model-based heuristic search methods. CDEoH explicitly models algorithm categories and jointly balances performance and category diversity in population management, enabling parallel exploration across multiple algorithmic paradigms. The authors conduct extensive experiments on representative combinatorial optimization problems, demonstrating that CDEoH significantly enhances evolutionary stability and achieves superior average performance. This breakthrough has significant implications for the development of automated algorithm generation techniques, particularly in the context of large language models. By leveraging the strengths of CDEoH, researchers and practitioners can unlock more efficient and effective solutions to complex optimization problems, with the potential to transform industries and applications.
Key Points
- ▸ CDEoH explicitly models algorithm categories to balance performance and category diversity
- ▸ CDEoH enables parallel exploration across multiple algorithmic paradigms
- ▸ CDEoH achieves superior average performance and enhances evolutionary stability
Merits
Increased evolutionary stability
CDEoH's ability to balance performance and category diversity enables the exploration of multiple algorithmic paradigms, reducing the likelihood of premature convergence and increasing the overall stability of the evolutionary process.
Improved performance
CDEoH's ability to explore multiple algorithmic paradigms and balance performance and category diversity enables the discovery of more effective solutions to complex optimization problems, leading to improved performance.
Scalability
CDEoH's ability to parallelize the exploration of multiple algorithmic paradigms makes it well-suited for large-scale optimization problems, enabling the exploration of multiple solutions simultaneously.
Demerits
Complexity
CDEoH's reliance on explicit modeling of algorithm categories and joint balancing of performance and category diversity may introduce additional complexity, making it more difficult to implement and fine-tune.
Dependence on large language models
CDEoH's reliance on large language models may limit its applicability to smaller or more resource-constrained environments, where the use of large language models may not be feasible.
Expert Commentary
The introduction of CDEoH marks a significant breakthrough in the development of automated algorithm generation techniques, particularly in the context of large language models. By leveraging the strengths of CDEoH, researchers and practitioners can unlock more efficient and effective solutions to complex optimization problems, with the potential to transform industries and applications. However, the complexity and dependence on large language models of CDEoH may limit its applicability to smaller or more resource-constrained environments. Further research is needed to explore the scalability and generalizability of CDEoH, as well as its potential applications in various fields.
Recommendations
- ✓ Further research is needed to explore the scalability and generalizability of CDEoH
- ✓ CDEoH should be applied to a wider range of optimization problems to demonstrate its effectiveness and versatility
Sources
Original: arXiv - cs.AI
