Evolutionary algorithm (EA) is a generic term for optimization algorithms inspired by biological optimization processes in the natural world. Although EAs are widely applied to complex real‐world problems because they do not require mathematical expression of target problems, theoretical design methods of EAs have not been established. To solve this issue, this paper proposes an approach of designing EAs within an algorithm framework in which mathematical characteristics are derived, and it also presents a concrete framework for that purpose. The presented framework, Regulated Evolution Strategies (RES) provides a stability analysis result that contributes to designing algorithms with expected behavior. The RES framework has a high degree of freedom in designing algorithms, so that it is possible to incorporate various contrivances such as local improvement of samples and reduction of constraint violations in RES‐based algorithms while maintaining the stability analysis result. Numerical experiments prove that the RES framework has a capability for designing high‐performance EAs. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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规范的进化策略：具有稳定性分析结果的进化算法框架

进化算法（EA）是自然界中生物优化过程所启发的优化算法的通用术语。尽管EA由于不需要对目标问题进行数学表达，所以已广泛应用于复杂的实际问题，但尚未建立EA的理论设计方法。为了解决这个问题，本文提出了一种在算法框架内设计EA的方法，该算法在其中推导了数学特征，并为此提供了一个具体的框架。所提出的框架，受控进化策略（RES）提供了稳定性分析结果，有助于设计具有预期行为的算法。RES框架在设计算法时具有高度的自由度，因此可以在基于RES的算法中纳入各种改进措施，例如样本的局部改进和减少约束违规，同时保持稳定性分析结果。数值实验证明，RES框架具有设计高性能EA的能力。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。