Boosted Surrogate Models in Evolutionary Optimization

Authors: Martin Holeňa
Publication year: 2009
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Information Technologies - Applications and Theory
Projects: Complexity of perceptron and kernel networks
Bibliographic citation: HOLEŇA, Martin. Boosted Surrogate Models in Evolutionary Optimization. In: VOJTÁŠ, P., ed. Information Technologies - Applications and Theory. Seňa: Pont, 2009, S. 15-22. ISBN 978-80-970179-2-7.
ID: 330006

Abstract

The paper deals with surrogate modelling, a modern approach to the optimization of empirical objective functions. The approach leads to a substantial decrease of time and costs of evaluation of the objective function, a property that is particularly attractive in evolutionary optimization. In the paper, an extension of surrogate modelling with regression boosting is proposed.