A Genetic Algorithm Based System with Different Crossover Operators for Solving the Course Allocation Problem of Universities-Reference-Cited by-同舟云学术

A Genetic Algorithm Based System with Different Crossover Operators for Solving the Course Allocation Problem of Universities

Published:2020 Issue: Volume: Page:149-160
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Container-title:New Trends in Computational Vision and Bio-inspired Computing
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Author:

Abhishek S.,Emmanuel Sunil Coreya,Rajeshwar G.,Jeyakumar G.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-41862-5_14

Reference21 articles.

1. Holland J.H. “Genetic Algorithms and Adaptation” In: Selfridge O.G., Rissland E.L., Arbib M.A. (eds) Adaptive Control of Ill-Defined Systems, NATO Conference Series (II Systems Science), Vol 16, 1984.

2. Umbarkar, Anantkumar and Sheth P, “Crossover Operators in Genetic Algorithms: A Review”, Journal on Soft Computing, Vol. 6, No. 1, 2015.

3. Saad Alharbi and Ibrahim Venkat, “A Genetic Algorithm Based Approach for Solving the Minimum Dominating Set of Queens Problem”, Journal of Optimization, 2017.

4. Sarika Goel and Vaishali Wadhwa, “A comparative analysis of pmx, pos and ox crossover operators for solving travelling salesman problem”, International Journal of Latest Research in Science and Technology, Vol. 2, No. 2, pp. 80–83, 2013.

5. Varun Kumar S G and R. Panneerselvam, “A study of crossover operators for genetic algorithms to solve VRP and its variants and new sinusoidal motion crossover operator”, International Journal of Computational Intelligence Research, Vol. 13, No. 7, pp. 1717–1733, 2017.

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