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A niching framework utilizing derandomized ES was introduced by Shir,<ref>Shir, O.M. (2008), "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.
An approach that does not use any radius for separating the population into subpopulations (or species) but employs the space topology instead is proposed in.<ref>C. Stoean, M. Preuss, R. Stoean, D. Dumitrescu (2010) [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5491155 Multimodal Optimization by means of a Topological Species Conservation Algorithm]. In IEEE Transactions on Evolutionary Computation, Vol. 14, Issue 6, pages 842–864, 2010.</ref>
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* D. Goldberg and J. Richardson. (1987) "[https://books.google.com/books?hl=en&lr=&id=MYJ_AAAAQBAJ&oi=fnd&pg=PA41&dq=%22Genetic+algorithms+with+sharing+for+multimodal+function+optimization%22&ots=XwsKxp3zHA&sig=5xS0qPl-83-h3FlY2vGoPN0kmK8#v=onepage&q=%22Genetic%20algorithms%20with%20sharing%20for%20multimodal%20function%20optimization%22&f=false Genetic algorithms with sharing for multimodal function optimization]". In Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application table of contents, pages 41–49. L. Erlbaum Associates Inc. Hillsdale, NJ, USA, 1987.
* A. Petrowski. (1996) "[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.33.8027&rep=rep1&type=pdf A clearing procedure as a niching method for genetic algorithms]". In Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, pages 798–803. Citeseer, 1996.
* Deb, K., (2001) "Multi-objective Optimization using Evolutionary Algorithms", Wiley ([https://books.google.com/books?id=OSTn4GSy2uQC&printsec=frontcover&dq=multi+objective+optimization&source=bl&ots=tCmpqyNlj0&sig=r00IYlDnjaRVU94DvotX-I5mVCI&hl=en&ei=fHnNS4K5IMuLkAWJ8OgS&sa=X&oi=book_result&ct=result&resnum=8&ved=0CD0Q6AEwBw#v=onepage&q&f=false Google Books)]
* F. Streichert, G. Stein, H. Ulmer, and A. Zell. (2004) "[http://neuro.bstu.by/ai/To-dom/My_research/Papers-0/For-courses/Niche/streichert03clustering.pdf A clustering based niching EA for multimodal search spaces]". Lecture Notes in Computer Science, pages 293–304, 2004.
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