Comparative study of genetic programming vs. neural networks for the classification of buried objects

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@InProceedings{Kobashigawa:2009:APSURSI,

• author = "Jill Kobashigawa and Hyoung-sun Youn and Magdy Iskander and Zhengqing Yun",
• title = "Comparative study of genetic programming vs. neural networks for the classification of buried objects",
• booktitle = "IEEE Antennas and Propagation Society International Symposium, APSURSI '09",
• year = "2009",
• month = jun,
• pages = "1--4",
• keywords = "genetic algorithms, genetic programming, buried objects classification, character classification problems, neural network structure optimization, untrained data robustness, buried object detection, image classification, neural nets",
• DOI = "doi:10.1109/APS.2009.5172386",
• ISSN = "1522-3965",
• abstract = "A comparative study of neural networks and genetic programming was conducted on six character classification problems. Based on the obtained results of the six problems, genetic programming showed better performance than neural networks in the various levels of problem difficulty. Genetic programming also showed robustness to untrained data, which caused difficulties for the neural networks. The optimization of the neural network structure was observed to be integral in obtaining both convergence and acceptable performance. A clear trend for structure optimization is not evident in the case of neural networks, and a global optimal solution may not be practical. On the other hand, because of the global searching nature of genetic programming, these problems with neural networks could be solved by using genetic programming.",
• notes = "Also known as \cite{5172386}",

}

Genetic Programming entries for Jill S K Nakatsu Hyoung-sun Youn Magdy F Iskander Zhengqing Yun

Citations