Evolutionary algorithms (EAs) are a set of optimization and machine learning techniques that find their inspiration in the biological processes of evolution established by Darwin [27] and other scientists in the ninenteenth century. Starting from a population of individuals that represent admissible solutions to a given problem through a suitable coding, these metaheuristics leverage the principles of variation by mutation, and recombination, and of selection of the best-performing individuals in a given environment. By iterating this process the system finds increasingly good solutions and generally solves the problem satisfactorily.