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== Background ==
Classical techniques of optimization would need multiple restart points and multiple runs in the hope that a different solution may be discovered every run, with no guarantee however <ref>Wong, K. C. (2015), [http://arxiv.org/abs/1508.00457 Evolutionary Multimodal Optimization: A Short Survey] arXig.org:1508.00457</ref>. [[Evolutionary algorithms]] (EAs) due to their population based approach, provide a natural advantage over classical optimization techniques. They maintain a population of possible solutions, which are processed every generation, and if the multiple solutions can be preserved over all these generations, then at termination of the algorithm we will have multiple good solutions, rather than only the best solution. Note that, this is against the natural tendency of EAs, which will always converge to the best solution, or a sub-optimal solution (in a rugged, “badly behaving” function).''' Finding''' and '''maintenance''' of multiple solutions is wherein lies the challenge of using EAs for multi-modal optimization. '''Niching''' <ref>Mahfoud, S. W. (1995), "Niching methods for genetic algorithms"</ref> is a generic term referred to as the technique of finding and preserving multiple stable ''niches'', or favorable parts of the solution space possibly around multiple solutions, so as to prevent convergence to a single solution.
The field of EAs today encompass [[genetic algorithm]]s (GAs), [[differential evolution]] (DE), [[particle swarm optimization]] (PSO), [[evolution strategy]] (ES) among others. Attempts have been made to solve multi-modal optimization in all these realms and most, if not all the various methods implement niching in some form or the other.
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