Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks

0347773 - ÚI 2011 RIV DE eng C - Conference Paper (international conference)
Bajer, L. - Holeňa, Martin
Surrogate Model for Continuous and Discrete Genetic Optimization Based on RBF Networks.
Intelligent Data Engineering and Automated Learning - IDEAL 2010. Berlin: Springer-Verlag, 2010 - (Fyfe, C.; Tino, P.; Garcia-Osorio, C.; Yin, H.), s. 251-258. Lecture Notes in Computer Science, 6283. ISBN 978-3-642-15380-8. ISSN 0302-9743.
[IDEAL 2010. International Conference on Intelligent Data Engineering and Automated Learning /11./. Paisley (GB), 01.09.2010-03.09.2010]
R&D Projects: GA ČR GD201/09/H057
Institutional research plan: CEZ:AV0Z10300504
Keywords : surrogate modelling * RBF networks * genetic algorithms * continuous and discrete variables
Subject RIV: IN - Informatics, Computer Science

Surrogate modelling has become a successful method improving the optimization of costly objective functions. It brings less accurate, but much faster means of evaluating candidate solutions. This paper describes a model based on radial basis function networks which takes into account both continuous and discrete variables. It shows the applicability of our surrogate model to the optimization of empirical objective functions for which mixing of discrete and continuous dimensions is typical. Results of testing with a genetic algorithm confirm considerably faster convergence in terms of the number of the original empirical fitness evaluations.
Permanent Link: http://hdl.handle.net/11104/0188472