Evolutionary Learning of Sigma-Pi Neural Trees and Its Application to Classification and Prediction

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

@Article{zhang:1996:els-pntacp,

• author = "B. T. Zhang",
• title = "Evolutionary Learning of Sigma-Pi Neural Trees and Its Application to Classification and Prediction",
• journal = "Journal of Korean Institute of Intelligent Systems",
• year = "1996",
• volume = "6",
• number = "2",
• pages = "13--21",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "1976-9172",
• URL = "http://ocean.kisti.re.kr/IS_mvpopo212L.do?method=list&poid=kfis&kojic=PJJNBT&sVnc=v6n2&&sFree====",
• size = "9 pages",
• abstract = "The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method.",
• notes = "In english",

}

Genetic Programming entries for Byoung-Tak Zhang

Citations