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Real Parameter Optimization Using Differential Evolution

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dc.description.abstract Over recent years, Evolutionary Algorithms (EA) have emerged as a practical approach to solve hard optimization problems presented in real life. The inherent advantage of EA over other types of numerical optimization methods lies in the fact that they require very little or no prior knowledge of the objective function. Information like differentiability or continuity is not necessary. The inspiration to learn from evolutionary processes and emulate them on a computer comes from varied directions, the most pertinent of which is the field of optimization. This paper presents one such Evolutionary Algorithm known as Differential Evolution (DE) and tests its performance on benchmark problems. Different variants of basic DE are discussed and their advantages and disadvantages are listed. This paper, through exhaustive experimentation, proposes an acceptable set of control parameters which may be applied to most of the benchmark functions to achieve good performance. en_US
dc.title Real Parameter Optimization Using Differential Evolution en_US
dc.date.accessioned 2013-12-05T17:07:59Z
dc.date.available 2013-12-05T17:07:59Z
dc.date.issued 2013-12-05
dc.identifier.uri http://hdl.handle.net/10365/23101
dc.subject Computer algorithms. en_US
dc.subject Evolution equations. en_US
dc.subject Stochastic processes -- Computer programs. en_US
dc.subject Evolution equations.
dc.subject Mathematical optimization.
dc.thesis.degree Paper (M.S.)-- North Dakota State University, 2013. en_US
dc.contributor.advisor Ludwig, Simone
dc.creator.author Dawar, Deepak
dc.degree.departmentCollege Master of Science / Computer Science, College of Science and Mathematics, 2013.
dc.date.created 2013

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