用遗传算法+大模型生成结构化任务，约束更严、结果更好
A Hybrid GA LLM Framework for Structured Task Optimization
- 把输出当基因，用大模型引导进化优化
- 在行程规划等任务中约束满足率显著提升
- 适合需要严格结构和高质量输出的场景
GA LLM 是一种融合遗传算法与大语言模型的混合框架，用于处理带严格约束的结构化生成任务。每个输出（如计划或报告）被视为一个基因，通过语言模型指导的选择、交叉和变异等进化操作，迭代优化解决方案。语言模型提供领域知识与创造性变化，遗传算法保障结构完整性和全局优化。该框架在行程规划、学术提纲和商业报告等任务中表现优异，持续生成结构良好且符合要求的结果。其模块化设计便于拓展至新任务。相比仅使用语言模型，GA LLM 在约束满足率和解质量方面均有明显提升。
原文摘要 · Abstract (English)
GA LLM is a hybrid framework that combines Genetic Algorithms with Large Language Models to handle structured generation tasks under strict constraints. Each output, such as a plan or report, is treated as a gene, and evolutionary operations like selection, crossover, and mutation are guided by the language model to iteratively improve solutions. The language model provides domain knowledge and creative variation, while the genetic algorithm ensures structural integrity and global optimization. GA LLM has proven effective in tasks such as itinerary planning, academic outlining, and business reporting, consistently producing well structured and requirement satisfying results. Its modular design also makes it easy to adapt to new tasks. Compared to using a language model alone, GA LLM achieves better constraint satisfaction and higher quality solutions by combining the strengths of both components.
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