Abstract
Differential Evolution (DE) is a vector population based and stochastic search optimization algorithm. DE converges faster, finds the global minimum independent to initial parameters, and uses few control parameters. DE is being trapped in local optima due to its greedy updating approach and inherent differential property. In order to maintain the proper balance between exploration and exploitation in the population a novel strategy named Guided Reproduction in Differential Evolution(GRDE) algorithm is proposed. In GRDE, two new phases are introduced into classical DE; first phase enhance the diversity while second phase exploits the search space without increasing the function evaluation. With the help of experiments over 20 well known benchmark problems 3 real world optimization problems; it has been shown that GRDE outperform as compared with classical DE.
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Rana, P.S., Sharma, H., Bhattacharya, M., Shukla, A. (2012). Guided Reproduction in Differential Evolution. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_12
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DOI: https://doi.org/10.1007/978-3-642-34859-4_12
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