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http://dx.doi.org/10.9708/jksci.2020.25.12.055

An Efficiency Analysis on Mutation Operation with TSP solved in Genetic Algorithm  

Yoon, Hoijin (Dept. of Computer Engineering, Hyupsung University)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.12, 2020 , pp. 55-61 More about this Journal
Abstract
Genetic Algorithm(GA) is applied to a problem that could not figure out its solution in a straightway. It is called as NP-hard problem. GA requires a high-performance system to be run on since the high-cost operations are needed such as crossover, selection, and mutation. Moreover, the scale of the problem domain is normally huge. That is why the straightway cannot be applied. To reduce the drawback of high-cost requirements, we try to answer if all the operations including mutation are necessary for all cases. In the experiment, we set up two cases of with/without mutation operations and gather the number of generations and the fitness of a solution. The subject in the experiment is Travelling Salesman Problem(TSP), which is one of the popular problems solved by GA. As a result, the cases with mutation operation are not faster and the solution is fitter than the case with mutation operation. From the result, the conclusion is that mutation operation does not always need for a better solution in a faster way.
Keywords
Mutation Operation; Genetic Algorithm; Travelling Salesman Problem; Efficiency; Fitness;
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1 Holland, J.H.. "Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence," MIT Press: Cambridge, MA, USA, pp.89-120, 1992.
2 M. Mitchell, "An Introduction to Genetic Algorithms," MIT Press, pp.128-130, 1999
3 Alan T Piszcz, Terence Soule, "A Survey of Mutation Techniques in Genetic Programming," Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 951-952, Seattle, Washington, USA, July 2006. DOI: 10.1145/1143997.1144165   DOI
4 E. Osaba, R. Carballedo, F. Diaz, E. Onieva, I. de la Iglesia, A. Perallos, "Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems", The Scientific World Journal, vol. 2014, Article ID 154676, 22 pages, 2014. DOI: 10.1155/2014/154676   DOI
5 Giancarlo Zaccone, "Natural Computing with Python," bpb publications, pp.85-118, 2019
6 Hee-Su Kim and Sung-Bae Cho, "An efficient genetic algorithm with less fitness evaluation by clustering," Proceedings of the 2001 IEEE Congress on Evolutionary Computation, pp.887-894, Seoul, South Korea, May 2001. DOI: 10.1109/CEC.2001.934284.   DOI
7 L. Wang, J. Zhang and H. Li, "An Improved Genetic Algorithm for TSP," Proceedings of 2007 International Conference on Machine Learning and Cybernetics, pp.925-928, Hong Kong, Aug. 2007. DOI: 10.1109/ICMLC.2007.4370274.   DOI
8 Clinton Sheppard, "Genetic Algorithms with Python," CreateSpace Independent Publishing Platform, pp.169-186, 2016
9 J. Lu, N. Fang, D. Shao and C. Liu, "An Improved Immune-Genetic Algorithm for the Traveling Salesman Problem," Proceedings of 3rd International Conference on Natural Computation (ICNC 2007), pp. 297-301, Haikou, Aug. 2007. DOI: 10.1109/ICNC.2007.217.   DOI
10 Y. Liu and J. Huang, "A Novel Genetic Algorithm and Its Application in TSP," Proceedings of 2008 IFIP International Conference on Network and Parallel Computing, pp. 263-266, Shanghai, Oct. 2008. DOI: 10.1109/NPC.2008.27   DOI
11 H. Yoon, "Fitness-Orientated Mutation Operators in Genetic Algorithm," IJITEE, Vol. 9, No. 4, pp.1769-1772, Feb. 2020. doi:10.35940/jijtee.D1692.029420   DOI
12 B. H. Hasan and M. S. Mustafa, "Comparative Study of Mutation Operators on the Behavior of Genetic Algorithms Applied to Non-deterministic Polynomial (NP) Problems," Proceedings of 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, pp. 7-12, Kuala Lumpur, Jan. 2011.DOI: 10.1109/ISMS.2011.11.   DOI