A Generic Form of Evolutionary Algorithms and Manifold Drift Concept
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Published:
Mar 5, 2019
Keywords:
Optimization Evolutionary algorithms opposite gradient search
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Abstract
Most of optimization problems in various fields are in NP-class. This implies that the time to find the optimum solution of any problem is obviously non-polynomial. Although the development of high speed computer architectures and the concept parallel computing is practically successful, some of these problems are constrained by the problem of tight data dependency which prevents the possibility of deploying a parallel architecture as well as processing to solve the problem. Evolutionary algorithms which are based on guessing solutions have been developed to find an acceptable solution in a short time. However, the processing time based on guessing to achieve the acceptable solution is unpredictable and uncontrollable. In this paper, we compare the guessing process in some popular algorithms to define a generic structure of searching process and solution finding process. This structure will help develop a new evolutionary algorithm. Furthermore, a new concept of manifold drift for avoiding the guessing process in order to speed up the solution search is also discussed.
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Lursinsap, C. (2019). A Generic Form of Evolutionary Algorithms and Manifold Drift Concept. INTERNATIONAL SCIENTIFIC JOURNAL OF ENGINEERING AND TECHNOLOGY (ISJET), 2(1), 1–10. Retrieved from https://ph02.tci-thaijo.org/index.php/isjet/article/view/175897
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Academic Article
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References
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[9] M. Dorigo, and L. Gambardella, “Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem”, IEEE TRANS- ACTIONS ON EVOLUTIONARY COMPUTATION, vol. 1, no. 1, Apr. 1997.
[10] P. Civicioglu, and E. Besdok, “A conceptual comparison of the Cuckoosearch, particle swarm optimization, differential evolution and artificial bee colony algorithms”, Artificial Intelligence Review, vol. 39, no. 4, pp. 315-346, Apr. 2013.
[11] M. Dorigoa, and C. Blumb, “Ant colony optimization theory: A survey”, Theoretical Computer Science, vol.344, pp. 243-278, Nov. 2005.
[12] S. Das and P. Suganthan, “Differential Evolution: A Survey of the State-of-the-Art”, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 15, no. 1, pp. 4-31. Feb. 2011.
[13] I. Boussad, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics”, Information Sciences, vol. 237, pp. 82-117, Jul. 2013
[14] H. Escalante, M. Montes, and L. Sucar, “Particle Swarm Model Selec- tion”, Journal of Machine Learning Research, vol. 10, pp. 405-440, 2009.
[15] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing”, Science, New Series, vol. 220, no. 4598, pp. 671-680, May. 1983.
[16] D. Bertsimass, and J. Tsitsiklis, “Simulated Annealing”, Statistical Science, vol. 8, no. 1, pp. 10-15, 1993.
[17] S. Mirjalili, and A. Lewis, “The Whale Optimization Algorithm”, Ad- vances in Engineering Software, vol. 95, pp. 51-67, May. 2016.
[18] S. He, Q. H. Wu, and J. R. Saunders, “Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior”, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, vol. 13, no. 5, pp. 973-990, Oct. 2009.
[19] Y. Shi, “Brain Storm Optimization Algorithm”, Y. Tan et al. (Eds.): ICSI 2011, Part I, LNCS 6728, pp. 303-309, 2011.
[20] W. Lva, C. Hea, D. Lia, S. Chenga, S. Luoa, and X. Zhanga, “Election campaign optimization algorithm”, Procedia Computer Science, vol.1(1) , pp. 1377-1386, May.2012.
[21] E. Fernandes, T. Martins, and A. Rocha, “Fish Swarm Intelligent Algorithm for Bound Constrained Global Optimization”, Proceedings of the International Conference on Computational and Mathematical Methods
in Science and Engineering, CMMSE 2009, 30 June, 1-3 July 2009.
[22] J. Holland, “Genetic Algorithms”, Scientific American, pp. 66-72, July. 1992.
[23] S. Forrest, “Genetic Algorithms: Principle of Natural Selection Applied to Computation”, Science, New Series, vol. 261(5123), pp. 872-878, Aug. 1993.
[24] T. Saenphon, S. Phimoltares, and C. Lursinsap, “Combining new Fast Opposite Gradient Search with Ant Colony Optimization for solving traveling salesman problem”, Engineering Applications of Artificial Intelligence, vol.35, pp. 324-334, Oct. 2014.