Genetic Algorithm with Species for Regularization Network Metalearning

0348394 - ÚI 2011 RIV SK eng C - Conference Paper (international conference)
Vidnerová, Petra - Neruda, Roman
Genetic Algorithm with Species for Regularization Network Metalearning.
Informačné Technológie - Aplikácie a Teória. Seňa: Pont, 2010 - (Pardubská, D.), s. 111-116. ISBN 978-80-970179-3-4.
[ITAT 2010. Conference on Theory and Practice of Information Technologies. Smrekovica (SK), 21.09.2010-25.09.2010]
R&D Projects: GA AV ČR KJB100300804
Institutional research plan: CEZ:AV0Z10300504
Keywords : regularization networks * kernel functions * genetic algorithms
Subject RIV: IN - Informatics, Computer Science

Regularization networks are one of the important methods for supervised learning. They benefit from very good theoretical background, although the presence of metaparameters is their drawback. The metaparameters are typically supposed to be given in advance and come ready as an input of the algorithm. Typically, they are set based on the task context by an experienced user. In this paper, we develop a method for finding optimal values of metaparameters, namely the type of kernel function, kernel parameters and regularization parameter. The method is based on co-evolutionary genetic algorithms with different species for different kind
Permanent Link: http://hdl.handle.net/11104/0188941