The origins of EAs can be traced back to the late 1950s, and since the 1970s several evolutionary methodologies have been proposed, mainly genetic algorithms, evolutionary programming, and evolution strategies. All of these approaches operate on a set of candidate solutions.
Selection represents the competition for resources among living beings.
Some are better than others and more likely to survive and to reproduce their genetic information. In evolutionary algorithms, natural selection is simulated by a stochastic selection process. Each solution is given a chance to reproduce a certain number of times, dependent on their quality. Here, quality is assessed by evaluating the individuals and assigning them scalar fitness values. Although the underlying principles are simple, these algorithms have proven themselves as a general, robust and powerful search mechanism.