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Evolutionary algorithms and, more generally, nature-inspired metaheuristics are gaining increasing favor as computational intelligence methods, very useful for global optimization problems.

The success of these population-based frameworks is mainly due to their flexibility and ease of adaptation to the most different and complex optimization problems, without requiring any special feature or condition to the objective functions and related constraints, like continuity, derivability, or convexity.

Discrete and combinatorial optimization problems, as well as mixed ones, are not a limit for this class of optimizers. Moreover, the requirement of uncertainty quantification in the search process, like in reliability-based optimization and robust design, is not a limit for this approach. 

Finally, population-based optimization algorithms can deal naturally with multiobjective problems, and this has made a big leap forward in the ability to effectively handle this class of problems possible. 

These advantages, together with the steady improvement of computer performance, are fostering their increased use in research and industry in a wide variety of engineering branches.