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An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[Theory of evolution]] particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.  

An '''Evolutionary algorithm''' (EA) is a stochastic numerical analysis that takes its inspiration from the [[theory of evolution]], particularly the optimization power of [[natural selection]]. The main thing that sets an evolutionary algorithm apart from other stochastic methods is the use of a [[fitness function]] to select for optimal solutions. New solutions are a created by allowing existing ones to breed with each other. Also many algorithms use random alterations in the coded solution similar to the biological principle of [[mutation]]. The fitness program selects solutions that better solve the problem and increases the frequency of that solution and its descendants in the over all population of solutions.  

==Types of Evolutionary algorithms==

==Types of Evolutionary algorithms==