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Evolutionary computation is an artificial intelligence subfield and closely linked to computational intelligence, involving lots of combinatorial optimization problems and continuous optimization.
It is employed in problem-solving systems that use computational models with evolutionary processes as the key design elements. It is an abstraction from the evolutionary concept in biology since it deals with methods and concepts that are continually and selectively evolving and optimizing.
Evolutionary computation is a general name for a group of problem-solving techniques whose principles are based on the theory of biological evolution, such as genetic inheritance and natural selection.
These techniques are applied to a variety of problems, from practical industry applications such as analytics and prediction algorithms to leading-edge scientific research such as protein folding.
Evolutionary computation is usually implemented on computer systems that are used to solve problems, implementing techniques such as evolutionary algorithms, differential evolution, genetic algorithms and harmony search.
Techniques in this field are used on problems that have too many variables for traditional algorithms to consider and in times where the approach to solving a particular problem is not well understood.