Abstract
In this paper, we provide a general introduction to the so-called multi-objective evolutionary algorithms, which are metaheuristic search techniques inspired on natural evolution that are able to deal with highly complex optimization problems having two or more objectives. In the first part of the paper, we provide some basic concepts necessary to make the paper self-contained, as well as a short review of the most representative multi-objective evolutionary algorithms currently available in the specialized literature. After that, a short review of applications of these algorithms in pattern recognition is provided. The final part of the paper presents some possible future research paths in this area as well as our conclusions.
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