In this article we provide a comprehensive review of the different evolutionary algorithm techniques used to address multimodal optimization problems, classifying them according to the nature of their approach. On the one hand there are algorithms that address the issue of the early convergence to a local optimum by differentiating the individuals of the population into groups and limiting their interaction, hence having each group evolve with a high degree of independence. On the other hand other approaches are based on directly addressing the lack of genetic diversity of the population by introducing elements into the evolutionary dynamics that promote new niches of the genotypical space to be explored. Finally, we study multi-objective optimization genetic algorithms, that handle the situations where multiple criteria have to be satisfied with no penalty for any of them. Very rich literature has arised over the years on these topics, and we aim at offering an overview of the most important techniques of each branch of the field.
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