Evolutionary Computation (EC) has emerged as a powerful field of Artificial Intelligence, inspired by nature's mechanisms of gradual development. However, EC approaches often face challenges such as stagnation, diversity loss, computational complexity, population initialization, and premature convergence. To overcome these limitations, researchers have integrated learning algorithms with evolutionary techniques. This integration harnesses the valuable data generated by EC algorithms during iterative searches, providing insights into the search space and population dynamics. Similarly, the relationship between evolutionary algorithms and Machine Learning (ML) is reciprocal, as EC methods offer exceptional opportunities for optimizing complex ML tasks characterized by noisy, inaccurate, and dynamic objective functions. These hybrid techniques, known as Evolutionary Machine Learning (EML), have been applied at various stages of the ML process. EC techniques play a vital role in tasks such as data balancing, feature selection, and model training optimization. Moreover, ML tasks often require dynamic optimization, for which Evolutionary Dynamic Optimization (EDO) is valuable. This paper presents the first comprehensive exploration of reciprocal integration between EDO and ML. The study aims to stimulate interest in the evolutionary learning community and inspire innovative contributions in this domain.

翻译：进化计算（EC）作为人工智能领域的重要分支，受自然界渐进发展机制启发而兴起。然而，EC方法常面临停滞、多样性丧失、计算复杂、种群初始化及过早收敛等挑战。为克服这些局限，研究人员将学习算法与进化技术相融合。这种集成利用EC算法在迭代搜索过程中产生的宝贵数据，为理解搜索空间和种群动态提供洞见。值得注意的是，进化算法与机器学习（ML）之间具有双向互补关系——EC方法为优化具有噪声、不精确及动态目标函数的复杂ML任务提供了独特机遇。这些被称为进化机器学习（EML）的混合技术已应用于ML流程的多个环节：EC技术在数据平衡、特征选择及模型训练优化等任务中发挥关键作用。此外，ML任务常需动态优化，而进化动态优化（EDO）在此领域具有重要价值。本文首次系统探讨了EDO与ML的双向融合关系，旨在激发进化学习领域的研究兴趣，促进该方向的创新性贡献。