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The application of multimodal optimization within ES was not explicit for many years, and has been explored only recently.
A niching framework utilizing derandomized ES was introduced by Shir,<ref>Shir, O.M. (2008), "[https://openaccess.leidenuniv.nl/handle/1887/12981 Niching in Derandomized Evolution Strategies and its Applications in Quantum Control]"</ref> '''proposing the [[CMA-ES]] as a niching optimizer for the first time'''. The underpinning of that framework was the selection of a peak individual per subpopulation in each generation, followed by its sampling to produce the consecutive dispersion of search-points. The ''biological analogy'' of this machinery is an ''alpha-male'' winning all the imposed competitions and dominating thereafter its ''ecological niche'', which then obtains all the sexual resources therein to generate its offspring.
Recently, an evolutionary [[multiobjective optimization]] (EMO) approach was proposed,<ref>Deb, K., Saha, A. (2010) "[https://dl.acm.org/doi/pdf/10.1145/1830483.1830568 Finding Multiple Solutions for Multimodal Optimization Problems Using a Multi-Objective Evolutionary Approach]" (GECCO 2010, In press)</ref> in which a suitable second objective is added to the originally single objective multimodal optimization problem, so that the multiple solutions form a '' weak pareto-optimal'' front. Hence, the multimodal optimization problem can be solved for its multiple solutions using an EMO algorithm. Improving upon their work,<ref>Saha, A., Deb, K. (2010) "A Bi-criterion Approach to Multimodal Optimization: Self-adaptive Approach " (Lecture Notes in Computer Science, 2010, Volume 6457/2010, 95–104)</ref> the same authors have made their algorithm self-adaptive, thus eliminating the need for pre-specifying the parameters.
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