Abstract
In this paper, we propose an evolutionary programming (EP) algorithm that incorporates both distribution-based and differential mutation operators in one algorithm. Distribution-based mutation operators are the ones that employ probability distribution functions such as Gaussian, Cauchy distributions for mutation. Thus the balance between exploration and exploitation is obtained by two different categories of mutation operators.
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Alam Anik, M.T., Ahmed, S. (2013). Evolutionary Programming Using Distribution-Based and Differential Mutation Operators. In: Mauri, G., Dennunzio, A., Manzoni, L., Porreca, A.E. (eds) Unconventional Computation and Natural Computation. UCNC 2013. Lecture Notes in Computer Science, vol 7956. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39074-6_23
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DOI: https://doi.org/10.1007/978-3-642-39074-6_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39073-9
Online ISBN: 978-3-642-39074-6
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