Editing Evolutionary computation (section)

{{Short description|Trial and error problem solvers with a metaheuristic or stochastic optimization character}}
{{For|the journal|Evolutionary Computation (journal)}}
[[File:Darwin image evolution from random patches.gif|thumb|Evolution of a population of random images. Each frame in the animation is a generation showing the best fitness individual with a genome made up of the greyscale level of each patch. Evolution repeatedly follows 1. evaluate fitness, 2. rank individuals and 3. include some genes from next highest fitness individual. Fitness is the error difference with an image of [[Charles Darwin]]. |alt=animation of random patches evolving into image of Charles Darwin]]
{{Evolutionary algorithms}}
{{Evolutionary biology}}
{{Use mdy dates|date=January 2012}}

'''Evolutionary computation''' from [[computer science]] is a family of [[algorithm]]s for [[global optimization]] inspired by [[biological evolution]], and the subfield of [[artificial intelligence]] and [[soft computing]] studying these algorithms. In technical terms, they are a family of population-based [[trial and error]] problem solvers with a [[metaheuristic]] or [[stochastic optimization]] character.

In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes as well as, depending on the method, mixing parental information. In biological terminology, a [[population]] of solutions is subjected to [[natural selection]] (or [[artificial selection]]), [[mutation]] and possibly [[Genetic recombination|recombination]]. As a result, the population will gradually [[evolution|evolve]] to increase in [[fitness (biology)|fitness]], in this case the chosen [[fitness function]] of the algorithm.

Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in [[computer science]]. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in [[evolutionary biology]] as an ''in silico'' experimental procedure to study common aspects of general evolutionary processes.