Abstract
A genetic algorithm has three main operators namely selection, crossover and mutation. Each operator has various sub operators. Selection of sub operator that can be applied on particular problem is difficult task. Thus this paper proposes a hybrid genetic algorithm (HGA). HGA algorithm finds the sub operators that can be applied on traveling salesman problem. After that it finds the threshold value. Based on threshold value it switches from one sub operator to other sub operator. The HGA algorithm score over existing genetic algorithm on traveling salesman problem on large number of cities.
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Acknowledgments
The author gratefully thankful to Rishab Rakshit student of SMIT who did simulation in summer project’16 at MUJ.
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Singhal, S., Goyal, H., Singhal, P., Grover, J. (2020). Hybrid Genetic Algorithm: Traveling Salesman Problem. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_48
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DOI: https://doi.org/10.1007/978-3-030-24322-7_48
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