Genetic Algorithms (GAs) are population based stochastic search and optimisation methods inspired by natural evolution. This chapter introduces the fundamental principles of GAs, including chromosome encoding, selection, crossover, mutation, and fitness evaluation. The standard simple GA framework is presented together with the role of genetic parameters such as crossover rate, mutation rate, and population size. The schema theorem is outlined to explain why short, low order, high fitness schemata tend to proliferate during evolution. The latter part of the chapter demonstrates how GAs can be applied to pattern recognition and binocular vision matching, illustrating their ability to search complex multidimensional spaces effectively. Through these examples, the chapter highlights the versatility and practical value of GAs in machine vision tasks.