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[[Heuristic (computer science)|Heuristic methods]] change accuracy by speed. Their goal is to find a good solution faster than the traditional methods, when they are too slow or fail in solving the problem. Usually they find local optimal instead of the optimal value; however, the values are considered close enough of the final solution. Examples of this kind of method is [[tabu search]] or [[Genetic algorithm]].<ref name=":0" />
Metamodels enable researchers to obtain reliable approximate model outputs without running expensive and time-consuming computer simulations. Therefore, the process of model optimization can take less computation time and cost. <ref>{{Cite journal|last=Yousefi|first=Milad|last2=Yousefi|first2=Moslem|last3=Ferreira|first3=Ricardo Poley Martins|last4=Kim|first4=Joong Hoon|last5=Fogliatto|first5=Flavio S.|title=Chaotic genetic algorithm and Adaboost ensemble metamodeling approach for optimum resource planning in emergency departments|url=http://linkinghub.elsevier.com/retrieve/pii/S0933365716305802|journal=Artificial Intelligence in Medicine|volume=84|pages=23–33|doi=10.1016/j.artmed.2017.10.002|pmid=29054572|year=2018}}</ref>
=== Stochastic approximation ===
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