Genetic Programming for Channel Equalisation

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{esparcia-alcazar:1999:GPce,

• author = "Anna Esparcia-Alcazar and Ken Sharman",
• title = "Genetic Programming for Channel Equalisation",
• booktitle = "Evolutionary Image Analysis, Signal Processing and Telecommunications: First European Workshop, EvoIASP'99 and EuroEcTel'99",
• year = "1999",
• editor = "Riccardo Poli and Hans-Michael Voigt and Stefano Cagnoni and Dave Corne and George D. Smith and Terence C. Fogarty",
• volume = "1596",
• series = "LNCS",
• pages = "126--137",
• address = "Goteborg, Sweden",
• publisher_address = "Berlin",
• month = "28-29 " # may,
• organisation = "EvoNet",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "3-540-65837-8",
• URL = "http://www.iti.upv.es/~anna/papers/evoiasp99.ps",
• DOI = "doi:10.1007/10704703_10",
• URL = "http://citeseer.ist.psu.edu/286482.html",
• abstract = "This paper is devoted to providing a comparison between classical and neural channel equalisation techniques and node gain Genetic Programming enhanced with Simulated Annealing (or GP+SA). Firstly, the shortcomings of existing techniques are exposed and the main requirements to demand of a new method enumerated. A description of the problem is followed by an account of particular cases of equalisation, exemplified by three channels, both linear and nonlinear. Results are obtained for these channels both with the proposed method and a classical technique, the Recursive Least Squares (RLS) algorithm, and they are further compared to those existing in the literature. The comparison shows the great potential of GP+SA, especially in the case of nonlinear channels. The main disadvantage of the proposed method, the computational effort involved, is also pointed out and it is concluded that, upon the whole, the method deserves further investigation.",
• notes = "EvoIASP99'99",

}

Genetic Programming entries for Anna Esparcia-Alcazar Kenneth C Sharman

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