Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks

0389195 - ÚI 2013 RIV DE eng C - Conference Paper (international conference)
Bajer, Lukáš - Holeňa, Martin
Surrogate Model for Mixed-Variables Evolutionary Optimization Based on GLM and RBF Networks.
SOFSEM 2013. Theory and Practice of Computer Science. Berlin: Springer, 2013 - (van Emde Boas, P.; Groen, F.; Italiano, G.; Nawrocki, J.; Sack, H.), s. 481-490. Lecture Notes in Computer Science, 7741. ISBN 978-3-642-35842-5. ISSN 0302-9743.
[SOFSEM 2013. Conference on Current Trends in Theory and Practice of Computer Science /39./. Špindlerův Mlýn (CZ), 26.01.2013-31.01.2013]
R&D Projects: GA ČR GAP202/11/1368; GA ČR GA201/08/0802
Grant - others:GA UK(CZ) 278511/2011
Institutional support: RVO:67985807
Keywords : surrogate modelling * RBF networks * genetic algorithms * mixed-variables optimization * continuous and discrete variables
Subject RIV: IN - Informatics, Computer Science

Approximation of costly objective functions by surrogate models is an increasingly popular method in many engineering optimization tasks. Surrogate models can substantially decrease the number of expensive experiments or simulations needed to achieve an optimal or near-optimal solution. In this paper, a novel surrogate model is presented. Compared to the most of the surrogate models reported in the literature, it has an advantage of explicitly dealing with mixed continuous and discrete variables. The model use radial basis function networks for continuous and clustering and a generalized linear model for the discrete covariates. The applicability of the model is shown on a benchmark problem, and the model’s regression performance is further measured on a dataset from a real-world application.
Permanent Link: http://hdl.handle.net/11104/0218075