Date of Award

2015

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Ludwig, Simone

Description

Over recent years, Evolutionary Algorithms have emerged as a practical approach to solve hard optimization problems in the fields of Science and Technology. 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 regarding differentiability or continuity of the objective function. The inspiration to learn evolutionary processes and emulate them on computer comes from varied directions, the most pertinent of which is the field of optimization. In most applications of EAs, computational complexity is a prohibiting factor. This computational complexity is due to number of fitness evaluations. This paper presents one such Evolutionary Algorithm known as Covariance Matrix Adaption Evolution Strategies (CMA ES) developed by Nikolaus Hansen, We implemented and evaluated its performance on benchmark problems aiming for least number of fitness evaluations and prove that the CMA-ES algorithm is efficient in solving optimization problems.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/25507

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