Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms
(GAs), have proved to be efficient when solving various problems; however, as a possible lack,
the GAs tend to provide a unique solution for the problem on which they are applied. Some non global
solutions discarded during the search of the best one could be acceptable under certain circumstances.
Most of the problems at the real world involve a search space with one or more global solutions and
multiple local solutions; this means that they are multimodal problems and therefore, if it is desired to
obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline
for adapting them correctly to the multimodality of such problems. The present chapter tries to establish,
firstly, the characterisation of the multimodal problems will be attempted. A global view of some of the
several approaches proposed for adapting the classic functioning of the GAs to the search of multiple
solutions will be also offered. Lastly, the contributions of the authors and a brief description of several
practical cases of their performance at the real world will be also showed.