大模型
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LLMDE：一种面向投资组合优化的大语言模型驱动差分进化算法
该论文提出 LLMDE 算法，利用 prompt engineering 让大语言模型根据优化反馈动态选择差分进化（DE）的变异策略并配置控制参数，以减少对手工超参数设计的依赖。作者在 CEC2022 基准上与标准 DE 及代表性元启发式算法对比，并结合因子分析与 K-means 聚类进行选股，将 LLMDE 用于带预算和最低预期收益约束的 CVaR 投资组合优化。结果显示其在基准测试上具有竞争力，并能持续为复杂约束优化任务生成高质量解，作者据此认为将 LLM 嵌入元启发式算法具有可行性。
AI推荐理由 arXiv 预印本，作者与摘要信息完整，方法描述具体（用 LLM 通过 prompt engineering 动态选择差分进化的变异策略与控制参数，并在 CEC2022 基准和 CVaR 投资组合优化上评估），属于可验证的研究工作。但仅为预印本、未见同行评审或代码开源，摘要只给出'competitive performance'等定性结论，缺少具体数值，且 LLM 辅助元启发式这一方向已有较多类似工作，新意有限，故兴趣分中等。
62
正文
Computer Science > Neural and Evolutionary Computing
arXiv:2609.16846 (cs)
[Submitted on 15 Sep 2026]
Title:LLMDE: A Large Language Model-Driven Differential Evolution Algorithm for Portfolio Optimization
Authors:Rong Chai, Vaclav Snasel, Xiaopeng Wang, Seyedali Mirjalili, Crina Grosan
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Abstract:This study proposes a Large Language Model-Driven Differential Evolution (LLMDE) algorithm to reduce the reliance on handcrafted hyperparameter design. The proposed algorithm leverages a prompt engineering strategy, allowing large language models (LLMs) to dynamically select mutation strategies and configure control parameters guided by optimization feedback, thus enhancing the performance of the DE algorithm. We evaluate the performance of LLMDE on the CEC2022 benchmark suite, comparing it with standard DE and representative metaheuristics. Furthermore, we employ factor analysis and K-means clustering for stock selection, and then apply LLMDE to solve the Conditional Value at Risk (CVaR) portfolio optimization problem using the selected stocks, subject to budget and minimum expected return constraints. Experimental results demonstrate that LLMDE achieves competitive performance on the benchmark suite while continuously generating high-quality solutions for complex constrained optimization tasks. These outcomes successfully demonstrate the viability of embedding LLMs within metaheuristics, paving a promising path toward the design of advanced LLM-assisted optimization techniques.
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Neural and Evolutionary Computing (cs.NE)
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arXiv:2609.16846 [cs.NE]
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arXiv:2609.16846v1 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2609.16846
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arXiv-issued DOI via DataCite (pending registration)
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From: Xiaopeng Wang [view email]
[v1]
Tue, 15 Sep 2026 08:39:44 UTC (6,690 KB)
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