In 2008 I followed the course Evolutionary Computing at De Montfort University. The course was lectured by Mario Gongora and Tim Watson. As a means of revising and to share some knowledge about this subject I try to provide a general outline of the subject on this page. But for now, some wikipedia stuff.
In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) that involves combinatorial optimization problems.
Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution. (Wikipedia, 2008)
A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (also known as evolutionary computation) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination). (Wikipedia, 2008)