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A '''genetic algorithm''' is a [[stochastic]] numerical algorithm used to solve optimization problems where [[analytic]] or step-wise optimization is impossible or intractable. Genetic algorithms are a popular subset of [[Evolutionary algorithms|Evolutionary algorithm]] which use common concepts in [[evolutionary biology]] and principles of the [[Theory of evolution]] such as: [[inheritance]], [[mutation]], [[selection]], and [[recombination]].

A '''genetic algorithm''' is a [[stochastic]] numerical algorithm used to solve optimization problems where [[analytic]] or step-wise optimization is impossible or intractable. Genetic algorithms are a popular subset of [[Evolutionary algorithm|Evolutionary algorithms]] which use common concepts in [[evolutionary biology]] and principles of the [[Theory of evolution]] such as: [[inheritance]], [[mutation]], [[selection]], and [[recombination]].

Solutions to a problem are randomly generated and then allowed to interact in a modeled environment. Various solutions can [[breed]] with each other producing new combinations, also at random intervals certain elements of the solution can randomly mutate. The fitness of any given solution to the problem is measured by a fitness function and those that are a better fit are selected for. This can either happen by increasing the rate of offspring reproduction in better solutions or by killing off solutions that perform worse.  

Solutions to a problem are randomly generated and then allowed to interact in a modeled environment. Various solutions can [[breed]] with each other producing new combinations, also at random intervals certain elements of the solution can randomly mutate. The fitness of any given solution to the problem is measured by a fitness function and those that are a better fit are selected for. This can either happen by increasing the rate of offspring reproduction in better solutions or by killing off solutions that perform worse.