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• W2093769902 abstract "The Gaussian Q-function is of great importance in the field of communications, where the noise is often characterized by the Gaussian distribution. However, no simple exact closed form of the Q-function is known. Consequently, a number of approximations have been proposed over the past several decades. In this paper, we use Genetic Programming with semantic based crossover to approximate the Q-function in two forms: the free and the exponential forms. Using this form, we found approximations in both forms that are more accurate than all previous approximations designed by human experts." @default.
• W2093769902 created "2016-06-24" @default.
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• W2093769902 date "2012-06-01" @default.
• W2093769902 modified "2023-09-26" @default.
• W2093769902 title "Evolving approximations for the Gaussian Q-function by Genetic Programming with semantic based crossover" @default.
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• W2093769902 doi "https://doi.org/10.1109/cec.2012.6256588" @default.
• W2093769902 hasPublicationYear "2012" @default.
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