# Kỹ thuật và công nghệ xây dựng công trình và cơ sở hạ tầng (Việt Nam và quốc tế)

This research paper has significantly analyzes convergence effects of 3 different genetic operations that will affect the speed and efficiency of solar tracking system to reach the highest intensity location under the sunlight coverage. The fitness value has identified the global minimum value for conventional GA with cloning and selective mutation method which is the most performing method as compared to other 2 methods. The proposed method improves search speed, good accuracy and approximate solution with the fitness value 0.017131 and 10.05V. 6. Reference [1] Holland, J. Adaptation in natural and artificial systems, Michigan: The University of Michigan Press, 1975 [2] Mitchell, M. An Introduction to Genetic Algorithms. Cambridge: The MIT Press,1996 [3] Koza, J.Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge: The MIT Press, 1992 [4] Whitley. An Overview of Evolutionary Algorithms. Journal of Information and Software Technology. 2001;43 : 817-831 [5] Khlaichom P, Sonthipermpoon K. Optimization of solar tracking system basedon genetic algorithms; 2006. http://www.thaiscience.info/. [6] Syamsiah Mashohor , Evaluation of Genetic Algorithm based Solar Tracking System for Photovoltaic Panels; ICSET,2008 [7] S.H.Jung, Selective Mutation for Genetic Algorithms, World Academy of Science, Engineering and Technology, vol 56, pp 478-481,2009 [8] J. Andre, P. Siarry, and T. Dognon, An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization, Advances in engineering software, vol. 32, no. 1, pp. 49–60, 2001. [9] J. E. Smith and T. C. Fogarty, Operator and parameter adaptation in genetic algorithms, Soft computing ; a fusion of foundations, methodologies and applications, vol. 92, no. 2, pp. 81–87, 1997. [10] S. H. Jung, Queen-bee evolution for genetic algorithms, Electronics Letters, vol. 39, pp. 575–576, Mar. 2003. [11] D. B. Fogel, An Introduction to Simulated Evolutionary Optimization,IEEE Transactions on Neural Networks, vol. 5, pp. 3–14, Jan. 1994. [12] J. Andre, P. Siarry, and T. Dognon, An improvement of the standard genetic

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