  Real world problems always have different multiple solutions. For instance,
optical engineers need to tune the recording parameters to get as many optimal
solutions as possible for multiple trials in the varied-line-spacing
holographic grating design problem. Unfortunately, most traditional
optimization techniques focus on solving for a single optimal solution. They
need to be applied several times; yet all solutions are not guaranteed to be
found. Thus the multimodal optimization problem was proposed. In that problem,
we are interested in not only a single optimal point, but also the others. With
strong parallel search capability, evolutionary algorithms are shown to be
particularly effective in solving this type of problem. In particular, the
evolutionary algorithms for multimodal optimization usually not only locate
multiple optima in a single run, but also preserve their population diversity
throughout a run, resulting in their global optimization ability on multimodal
functions. In addition, the techniques for multimodal optimization are borrowed
as diversity maintenance techniques to other problems. In this chapter, we
describe and review the state-of-the-arts evolutionary algorithms for
multimodal optimization in terms of methodology, benchmarking, and application.
