Evolving Kernel Functions with Particle Swarms and Genetic Programming

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

@InProceedings{conf/flairs/SchuhAS12,

• author = "Michael A. Schuh and Rafal A. Angryk and John Sheppard",
• title = "Evolving Kernel Functions with Particle Swarms and Genetic Programming",
• booktitle = "Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference, 2012",
• year = "2012",
• editor = "G. Michael Youngblood and Philip M. McCarthy",
• pages = "80--85",
• address = "Marco Island, Florida",
• month = may # " 23-25",
• keywords = "genetic algorithms, genetic programming, SVM, PSO",
• isbn13 = "978-1-57735-558-8",
• publisher = "AAAI Press",
• URL = "http://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS12/paper/view/4479/4770.pdf",
• size = "6 pages",
• abstract = "The Support Vector Machine has gained significant popularity over recent years as a kernel-based supervised learning technique. However, choosing the appropriate kernel function and its associated parameters is not a trivial task. The kernel is often chosen from several widely-used and general-purpose functions, and the parameters are then empirically tuned for the best results on a specific data set. This paper explores the use of Particle Swarm Optimisation and Genetic Programming as evolutionary approaches to evolve effective kernel functions for a given dataset. Rather than using expert knowledge, we evolve kernel functions without human-guided knowledge or intuition. Our results show consistently better SVM performance with evolved kernels over a variety of traditional kernels on several datasets.",
• bibdate = "2012-07-11",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/flairs/flairs2012.html#SchuhAS12",

}

Genetic Programming entries for Michael A Schuh Rafal A Angryk John W Sheppard

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