Evolutionary multimodal optimization: Difference between revisions

Revision as of 04:22, 2 December 2017

Revision as of 19:07, 26 January 2018

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In [[applied mathematics]], '''multimodal optimization''' deals with [[Mathematical optimization|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,<ref>Wong, K. C. (2015), [httphttps://arxiv.org/abs/1508.00457 Evolutionary Multimodal Optimization: A Short Survey] arXiv preprint arXiv:1508.00457</ref> wherein the chapter of Shir<ref>Shir, O.M. (2012), [https://link.springer.com/book/10.1007/978-3-540-92910-9 Niching in Evolutionary Algorithms]</ref> and the book of Preuss<ref>Preuss, Mike (2015), [https://www.springer.com/de/book/9783319074061 Multimodal Optimization by Means of Evolutionary Algorithms]</ref> cover the topic in more detail.

== Motivation ==