A study of fluctuations and confidence of implementation in genetic algorithm optimized network in data centre
Study of fluctuation in genetic algorithm has been a sub-objective in genetic algorithm implementations. The reliability of genetic algorithm may vary based on implementation case, hence it is necessary to investigate its performance pattern for each implementation case. The purpose of this study is...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
IOS Press
2018
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044388061&doi=10.3233%2fIDT-170320&partnerID=40&md5=9d549ba3f09997816726f089c264e12c http://eprints.utp.edu.my/21911/ |
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| Summary: | Study of fluctuation in genetic algorithm has been a sub-objective in genetic algorithm implementations. The reliability of genetic algorithm may vary based on implementation case, hence it is necessary to investigate its performance pattern for each implementation case. The purpose of this study is to observe the reliability of genetic algorithm in our previously simulated network optimization in a data centre. Our findings agree with the nature of genetic algorithm and other previous researchers, where it is found that the fluctuation of fitness values in our case happened randomly in general, but it had higher probability with small population sizes. However, regardless of fluctuations that in average occurred during early stage of population generation, the optimal solutions with near-maximum fitness values were able to be generated. This fact has proven the robustness of genetic algorithm itself. Alongside the fluctuation studies, this paper also presents the results of standard deviation and 95 confidence interval calculations towards the true mean of best solutions' fitness values. The computed standard deviations reflect the consistency of the adjusted GA properties in finding the best optimal solutions when run repeatedly. Afterward, it is also concluded from the confidence intervals analysis that 95 of the time, the fitness values of the discovered solutions, which represent multiple network cards' optimal configurations will be between near-maximum fitness value of 100 Mbps. Thus, our methodology to improve data centre's network, through simultaneous multiple network cards optimization can be expected to be highly achieving. © 2018 - IOS Press and the authors. All rights reserved. |
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