In applied mathematics, multimodal optimization deals with optimization tasks that involve finding all or most of the multiple (at least locally optimal) solutions of a problem, as opposed to a single best solution. Evolutionary multimodal optimization is a branch of evolutionary computation, which is closely related to machine learning. Wong provides a short survey,[1] wherein the chapter of Shir[2] and the book of Preuss[3] cover the topic in more detail.
- ^ Wong, K. C. (2015), Evolutionary Multimodal Optimization: A Short Survey arXiv preprint arXiv:1508.00457
- ^ Shir, O.M. (2012), Niching in Evolutionary Algorithms Archived 2016-03-04 at the Wayback Machine
- ^ Preuss, Mike (2015), Multimodal Optimization by Means of Evolutionary Algorithms
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