This chapter gives an overview of Evolutionary Computation and starts by positioning this family of evolutionary-inspired stochastic search approaches within the broad field of modern heuristic methods. In the introductory section, attention is given to three classes that historically form the field: Genetic Algorithms, Evolutionary Programming, and Evolution Strategies. The next section starts with a unified treatment of these Evolutionary Algorithms in the context of a generate-and-test framework. After that, their key principles and components such as the use of a population of candidates, a parametric solution representation, and associated operators of selection and variation will be described in detail. This is then followed by a section detailing advanced mechanisms to automate the control of parameter settings required for operations of Evolutionary Algorithms. The last section closes with some remarks discussing the difference and similarity in the search behaviour between exact methods and Evolutionary Algorithms in the context of continuous optimization problems.