TY - GEN
T1 - Evolving approximations for the Gaussian Q-function by genetic programming with semantic based crossover
AU - Phong, Dao Ngoc
AU - Uy, Nguyen Quang
AU - Hoai, Nguyen Xuan
AU - McKay, R. I.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84866876708&partnerID=8YFLogxK
U2 - 10.1109/CEC.2012.6256588
DO - 10.1109/CEC.2012.6256588
M3 - Conference contribution
SN - 9781467315098
T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
Y2 - 10 June 2012 through 15 June 2012
ER -