Evolving Approximations for the Gaussian Q-function by Genetic Programming with Semantic Based Crossover

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Dao:2012:CEC,

• title = "Evolving Approximations for the {Gaussian Q-function} by Genetic Programming with Semantic Based Crossover",
• author = "Ngoc Phong Dao and {Quang Uy Nguyen} and {Xuan Hoai Nguyen} and R I (Bob) McKay",
• pages = "2515--2520",
• booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary Computation",
• year = "2012",
• editor = "Xiaodong Li",
• month = "10-15 " # jun,
• DOI = "doi:10.1109/CEC.2012.6256588",
• address = "Brisbane, Australia",
• ISBN = "0-7803-8515-2",
• keywords = "genetic algorithms, genetic programming, Computational Intelligence in Communications and Networking (IEEE-CEC), Real-world applications",
• abstract = "The Gaussian Q-function is of great importance in the field of communications, where the noise is often characterised 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.",
• notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.",

}

Genetic Programming entries for Ngoc Phong Dao Quang Uy Nguyen Nguyen Xuan Hoai R I (Bob) McKay

Citations