ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback
Summary: arXiv:2604.04940v1 Announce Type: new
Abstract: Designing effective heuristics for NP-hard combinatorial optimization problems remains a challenging and expertise-intensive task. Existing applications of large language models (LLMs) primarily rely on one-shot code synthesis, yielding brittle heuristics that underutilize the models’ capacity for iterative reasoning. We propose ReVEL: Multi-Turn Reflective LLM-Guided Heuristic Evolution via Structured Performance Feedback, a hybrid framework that embeds LLMs as interactive, multi-turn reasoners within an evolutionary algorithm (EA).
The core of ReVEL lies in two mechanisms:
- Performance-profile grouping: This mechanism clusters candidate heuristics into behaviorally coherent groups, allowing for compact and informative feedback to the LLM.
- Multi-turn, feedback-driven reflection: The LLM analyzes group-level behaviors and generates targeted heuristic refinements based on the feedback received.
These refinements are selectively integrated and validated by an EA-based meta-controller that adaptively balances exploration and exploitation. The innovative approach adopted by ReVEL not only enhances the quality of heuristics but also increases their robustness and diversity.
Significance of the Research
Combinatorial optimization problems are prevalent across various fields, from logistics to scheduling, and are often NP-hard, meaning they cannot be solved efficiently using traditional methods. The development of effective heuristics is crucial, as they provide approximate solutions in a reasonable timeframe. However, the existing methods for creating these heuristics often result in solutions that are not sufficiently robust or adaptable to varying problem instances.
ReVEL addresses these challenges by leveraging the strengths of LLMs in a multi-turn interaction framework. By allowing the model to engage in iterative reasoning, the framework significantly improves the quality of heuristics generated. This is particularly important in scenarios where a single heuristic may not suffice due to the complexity of the problem at hand.
Experimental Validation
To validate the efficacy of ReVEL, extensive experiments were conducted on standard benchmarks in combinatorial optimization. The results demonstrated that the heuristics produced by ReVEL consistently outperformed those generated by traditional methods and even some of the strongest baseline heuristics.
- Statistical analysis indicated significant improvements in both robustness and diversity of the heuristics.
- Furthermore, the integration of feedback-driven reflection allowed for more nuanced adaptations of the heuristics, which is often missing in one-shot synthesis approaches.
Conclusion
The introduction of ReVEL marks a significant advancement in the field of automated heuristic design. By combining the iterative reasoning capabilities of LLMs with evolutionary algorithms, researchers can expect not only better-performing heuristics but also a more systematic approach to problem-solving in combinatorial optimization. This research opens new avenues for exploring how machine learning techniques can enhance traditional optimization methods, paving the way for future innovations in the field.