用大模型模拟择偶思维，让进化算法更聪明地配对个体。
PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms
- 用大模型综合评估基因多样性、适应度和交叉兼容性来智能选配对
- 在多个旅行商问题实例上优化差距更低，收敛更快
- 适合想用大模型提升进化算法性能的研究者
进化算法（EAs）通常采用随机或简单的选择方法，限制了其对解空间的探索能力并影响收敛至最优解。交叉或变异过程中的随机性可能降低模型演化效率。本文提出偏好对齐个体互惠（PAIR）策略，利用大语言模型（LLM）模拟人类择偶逻辑，为进化算法的配对过程引入智能。PAIR通过提示LLM评估种群中个体的基因多样性、适应度水平及交叉兼容性，指导更明智的配对决策。我们在多个旅行商问题（TSP）实例上将PAIR与近期发表的基于大模型的进化算法（LMEA）进行对比，结果表明：PAIR显著优于LMEA，实现更低的最优性差距和更快的收敛速度。该优势在结合闪思模型时尤为明显，展现出更强的种群多样性，有助于跳出局部最优。总体而言，PAIR通过复杂偏好建模，为大模型驱动的进化算法选择提供了新的上下文学习策略，推动了大模型引导优化的发展。
原文摘要 · Abstract (English)
Evolutionary Algorithms (EAs) employ random or simplistic selection methods, limiting their exploration of solution spaces and convergence to optimal solutions. The randomness in performing crossover or mutations may limit the model's ability to evolve efficiently. This paper introduces Preference-Aligned Individual Reciprocity (PAIR), a novel selection approach leveraging Large Language Models to emulate human-like mate selection, thereby introducing intelligence to the pairing process in EAs. PAIR prompts an LLM to evaluate individuals within a population based on genetic diversity, fitness level, and crossover compatibility, guiding more informed pairing decisions. We evaluated PAIR against a baseline method called LLM-driven EA (LMEA), published recently. Results indicate that PAIR significantly outperforms LMEA across various TSP instances, achieving lower optimality gaps and improved convergence. This performance is especially noticeable when combined with the flash thinking model, demonstrating increased population diversity to escape local optima. In general, PAIR provides a new strategy in the area of in-context learning for LLM-driven selection in EAs via sophisticated preference modelling, paving the way for improved solutions and further studies into LLM-guided optimization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。