Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25309
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Authors: Ullah, Amjad
Li, Jingpeng
Hussain, Amir
Shen, Yindong
Title: Genetic optimization of fuzzy membership functions for cloud resource provisioning
Citation: Ullah A, Li J, Hussain A & Shen Y (2017) Genetic optimization of fuzzy membership functions for cloud resource provisioning In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), Piscataway, NJ, USA: IEEE. 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 6.12.2016 - 9.12.2016, Athens, Greece.
Issue Date: 13-Feb-2017
Conference Name: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)
Conference Dates: 2016-12-06T00:00:00Z
Conference Location: Athens, Greece
Abstract: The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings.
Status: Book Chapter: author post-print (pre-copy editing)
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
URL: http://ieeexplore.ieee.org/document/7850088/

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