Genetic Algorithms were devised in the 60s and are commonly related to a theory known as Evolutionary Programming. The concept behind these algorithms is based on the adaptive operations performed by nature, when a population of "chromosomes" moves toward a new population through "natural selection" (according to Darwin). During this procedure, the chromosomes can reproduce, mutate and cross-breed in order to produce better and stronger ("fitter") offsprings, some of which will prevail in the end.

 Genetic algorithms are commonly used in optimization and adaptation tasks, where the best selection of possible solutions is required. Their mechanism of problem solving does not follow a mathematical logic, but a biological one. Therefore, algorithms of this type have the flexibility and freedom to select the desired optimal solution based on the current system (problem) design specifications.