This article provides a comprehensive introduction to evolution strategies (ES), a major branch of evolutionary computation. Originating in Germany in the 1960s, ES has evolved into a significant area of research in computational intelligence. The paper traces the history of ES, explains its philosophical foundations, and outlines basic algorithms and design principles for variation and selection operators. It also discusses theoretical issues and future research directions in ES. The article begins by highlighting the influence of biological inspiration on computer science, particularly in algorithm engineering. It describes three paradigms that have survived in the field: artificial neural networks, fuzzy logic, and evolutionary computation (EC). EC aims to leverage collective phenomena in adaptive populations, such as birth, death, variation, and selection, in an iterative loop. Evolutionary algorithms (EA) have three main variants: evolutionary programming (EP), genetic algorithms (GA), and evolution strategies (ES). ES was developed by students at the Technical University of Berlin. The paper provides an overview of ES, its history, basic ideas, and current state. It is organized into sections discussing the history of ES research, basic principles of ES algorithms, adaptation approaches, theoretical aspects, and future directions. Section 2 outlines the history of ES research, while Section 3 introduces the basic ideas and principles of ES algorithms, including mutation, recombination, and selection operators. Section 4 discusses different approaches to adaptation, and Section 5 covers theoretical aspects such as evolutionary dynamics, convergence, and time complexity. The paper concludes with an outlook on the future of ES research.This article provides a comprehensive introduction to evolution strategies (ES), a major branch of evolutionary computation. Originating in Germany in the 1960s, ES has evolved into a significant area of research in computational intelligence. The paper traces the history of ES, explains its philosophical foundations, and outlines basic algorithms and design principles for variation and selection operators. It also discusses theoretical issues and future research directions in ES. The article begins by highlighting the influence of biological inspiration on computer science, particularly in algorithm engineering. It describes three paradigms that have survived in the field: artificial neural networks, fuzzy logic, and evolutionary computation (EC). EC aims to leverage collective phenomena in adaptive populations, such as birth, death, variation, and selection, in an iterative loop. Evolutionary algorithms (EA) have three main variants: evolutionary programming (EP), genetic algorithms (GA), and evolution strategies (ES). ES was developed by students at the Technical University of Berlin. The paper provides an overview of ES, its history, basic ideas, and current state. It is organized into sections discussing the history of ES research, basic principles of ES algorithms, adaptation approaches, theoretical aspects, and future directions. Section 2 outlines the history of ES research, while Section 3 introduces the basic ideas and principles of ES algorithms, including mutation, recombination, and selection operators. Section 4 discusses different approaches to adaptation, and Section 5 covers theoretical aspects such as evolutionary dynamics, convergence, and time complexity. The paper concludes with an outlook on the future of ES research.