This article provides a comprehensive introduction to evolution strategies (ES), a key branch of evolutionary computation. Originating in Germany in the 1960s, ES have evolved from a set of rules for automatic design and analysis of experiments to sophisticated optimization techniques. The authors, Hans-Georg Beyer and Hans-Paul Schwefel, trace the historical development of ES, explaining the philosophical background that underpins their implementation. They cover fundamental ES algorithms, design principles for variation and selection operators, and theoretical issues. The paper also discusses future research directions in ES, emphasizing the importance of understanding the adaptive behavior of these operators. The content is organized into sections that delve into the history, basic ideas, performance aspects, adaptation approaches, and theoretical aspects of ES, concluding with an outlook on potential future developments.This article provides a comprehensive introduction to evolution strategies (ES), a key branch of evolutionary computation. Originating in Germany in the 1960s, ES have evolved from a set of rules for automatic design and analysis of experiments to sophisticated optimization techniques. The authors, Hans-Georg Beyer and Hans-Paul Schwefel, trace the historical development of ES, explaining the philosophical background that underpins their implementation. They cover fundamental ES algorithms, design principles for variation and selection operators, and theoretical issues. The paper also discusses future research directions in ES, emphasizing the importance of understanding the adaptive behavior of these operators. The content is organized into sections that delve into the history, basic ideas, performance aspects, adaptation approaches, and theoretical aspects of ES, concluding with an outlook on potential future developments.