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Comparing and Contrasting Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategies

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dc.contributor.advisor Wineberg, Mark
dc.contributor.author Opawale, Samuel
dc.date.accessioned 2018-05-09T19:41:07Z
dc.date.available 2018-05-09T19:41:07Z
dc.date.copyright 2018-05
dc.date.created 2018-05-04
dc.date.issued 2018-05-09
dc.identifier.uri http://hdl.handle.net/10214/13036
dc.description.abstract Evolutionary algorithms (EA) comprises population based algorithms that uses biologically inspired operators for optimization. DE and CMAES/IPOP are two powerful forms of EA that act on real numbers in order to provide solutions to multidimensional problems. Previously, researchers have tried to compare these two algorithms head-to-head, but no attempt has been made to compare and contrast the underlying mechanisms of these algorithms in order to better understand their effects and functionalities. The selection operator for CMA-ES was modified to make it more DElike by adding elitism selection instead of (μ, λ)). A new selection operator,here, was added to ES. We noticed an improvement with IPOP when here and elitism were used singly. In combination, the effect becomes remarkable, producing often a several orders of magnitude improvement in convergence time. One function, Levy, cause IPOP to stall when elitism was added. The reason for this is currently unknown. en_US
dc.language.iso en en_US
dc.rights Attribution-NonCommercial 2.5 Canada *
dc.rights.uri http://creativecommons.org/licenses/by-nc/2.5/ca/ *
dc.title Comparing and Contrasting Differential Evolution (DE) and Covariance Matrix Adaptation Evolution Strategies en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
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