Recent advances in data-driven evolutionary algorithms (EAs) have demonstrated the potential of leveraging historical data to improve optimization accuracy and adaptability. Despite these advancements, existing methods remain reliant on handcrafted process-level operators. In contrast, Evolutionary Generative Optimization (EvoGO) is a fully data-driven framework designed from the objective level, enabling autonomous learning of the entire search process. EvoGO streamlines the evolutionary optimization process into three stages: data preparation, model training, and population generation. The data preparation stage constructs a pairwise dataset to enrich training diversity without incurring additional evaluation costs. During model training, a tailored generative model learns to transform inferior solutions into superior ones. In the population generation stage, EvoGO replaces traditional reproduction operators with a scalable and parallelizable generative mechanism. Extensive experiments on numerical benchmarks, classical control problems, and high-dimensional robotic tasks demonstrate that EvoGO consistently converges within merely 10 generations and substantially outperforms a wide spectrum of optimization approaches, including traditional EAs, Bayesian optimization, and reinforcement learning based methods. Code is available at: https://github.com/EMI-Group/evogo

翻译：近年来，数据驱动进化算法（EAs）的研究进展表明，利用历史数据能够提升优化精度与适应性。尽管已有方法取得进步，它们仍依赖于人工设计的过程级算子。与之相对，进化生成优化（EvoGO）是一个从目标层面设计的完全数据驱动框架，能够自主学习整个搜索过程。EvoGO将进化优化流程简化为三个阶段：数据准备、模型训练与种群生成。数据准备阶段构建配对数据集以增强训练多样性，同时避免引入额外的评估开销。在模型训练阶段，一个定制的生成模型学习如何将劣质解转化为优质解。在种群生成阶段，EvoGO以可扩展且可并行的生成机制取代了传统的繁殖算子。在数值基准测试、经典控制问题以及高维机器人任务上进行的大量实验表明，EvoGO仅需约10代即可稳定收敛，并在性能上显著超越包括传统进化算法、贝叶斯优化以及基于强化学习的方法在内的多种优化方法。代码发布于：https://github.com/EMI-Group/evogo