The iterative search process of evolutionary algorithms (EAs) encapsulates optimization knowledge within historical populations and fitness evaluations. Effective utilization of this knowledge is crucial for facilitating knowledge transfer and online adaptation. However, current research typically addresses these goals in isolation and faces distinct limitations: evolutionary sequential transfer optimization often suffers from incomplete utilization of prior knowledge, while adaptive strategies, utilizing real-time knowledge, are limited to tailoring specific evolutionary operators. To simultaneously achieve these two capabilities, we introduce the Optimization Knowledge Adaptation Evolutionary Model (OKAEM), a unified learnable evolutionary framework capable of adaptively updating parameters based on available optimization knowledge. By parameterizing evolutionary operators via attention mechanisms, OKAEM enables learnable update rules that facilitate the utilization of optimization knowledge via two phases: pre-training to integrate extensive prior knowledge for efficient transfer, and adaptive optimization to dynamically update parameters based on real-time knowledge. Experimental results confirm that OKAEM significantly outperforms state-of-the-art sequential transfer methods across 12 transfer scenarios via pre-training, and surpasses advanced learnable EAs solely through its self-tuning mechanism in prior-free settings. Beyond demonstrating practical utility in prompt tuning for vision-language models, ablation studies validate the necessity of the learnable components, while visualization analyses reveal the model's capacity to autonomously discover interpretable evolutionary principles. The code can be accessed at https://gitee.com/Anonymity_Paper/code-of-okaem.

翻译：进化算法（EAs）的迭代搜索过程将优化知识封装于历史种群与适应度评估中。有效利用这些知识对促进知识迁移和在线自适应至关重要。然而，当前研究通常分别处理这些目标并面临显著局限：进化序列迁移优化常因先验知识利用不完整而受限，而利用实时知识的自适应策略仅能定制特定进化算子。为同时实现这两项能力，我们提出优化知识自适应进化模型（OKAEM），这是一种统一的可学习进化框架，能够基于可用优化知识自适应更新参数。通过注意力机制参数化进化算子，OKAEM实现了可学习的更新规则，并经由两个阶段促进优化知识利用：预训练阶段整合广泛先验知识实现高效迁移，自适应优化阶段基于实时知识动态更新参数。实验结果表明，OKAEM在12个迁移场景中通过预训练显著优于最先进的序列迁移方法，并在无先验设置下仅凭自调节机制超越先进的可学习EAs。除在视觉语言模型的提示调优中展示实际效用外，消融研究验证了可学习组件的必要性，可视化分析则揭示模型能自主发现可解释的进化原理。代码可通过https://gitee.com/Anonymity_Paper/code-of-okaem获取。