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The goal of this paper is to provide an overview of a variety of evolutionary algorithms, comparing their efficiency on fuzzy rule-based inference and learning. Fuzzy rule-based inference can be used to model a desirable outward behavior of a system when given a specific input, which, in the case of this comparative study, is determined by a set of samples, generated by sufficiently complex objective functions. Optimizing a fuzzy rule-based inference system is a matter of finding a rule base that is as close to imitating the desired behavior as possible. While the specific applications of evolutionary methods are endless, the objective functions used here remain general in nature.