@inproceedings{scholars8825,
            year = {2017},
            note = {cited By 0; Conference of 1st EAI International Conference on Computer Science and Engineering, COMPSE 2016 ; Conference Date: 11 November 2016 Through 12 November 2016; Conference Code:130814},
       publisher = {EAI},
         journal = {COMPSE 2016 - 1st EAI International Conference on Computer Science and Engineering},
           title = {A study of fluctuations in genetic algorithm optimized network in data centre},
            isbn = {9781631901362},
          author = {Nurika, O. and Hassan, M. F. and Zakaria, N. and Jung, L. T.},
        abstract = {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. Previous researchers found fluctuation as random occurrence, mainly within small population. This paper's fluctuation observation revolves around our recent optimization of data centre's network. Our findings agree with the nature of genetic algorithm and other researches, 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 size. However, regardless of fluctuations that in average occurred during early stage of population generation, the near-optimal solutions with near maximum fitness values were able to be generated. This fact has proven the robustness of genetic algorithm itself.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032336611&partnerID=40&md5=f33606e4e5c6db54609b581b1852a633},
        keywords = {Genetic algorithms; Population statistics, Algorithm implementation; Drift; Fluctuation; Near-optimal solutions; Network card; Performance patterns; Simulated networks; Small population, Optimization}
}