Bayesian optimization algorithms for dynamic problems

Authors:
Miloš Kobliha;Josef Schwarz;Jiří Očenášek
Affiliations:
Department of Computer Systems, Brno University of Technology, Faculty of Information Technology, Brno, CZ;Department of Computer Systems, Brno University of Technology, Faculty of Information Technology, Brno, CZ;Kimotion Technologies, Boutersem, Belgium
Venue:
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Year:
2006

Citing 3
Cited 0

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Abstract

This paper is an experimental study investigating the capability of Bayesian optimization algorithms to solve dynamic problems. We tested the performance of two variants of Bayesian optimization algorithms – Mixed continuous-discrete Bayesian Optimization Algorithm (MBOA), Adaptive Mixed Bayesian Optimization Algorithm (AMBOA) – and new proposed modifications with embedded Sentinels concept and Hypervariance. We have compared the performance of these variants on a simple dynamic problem – a time-varying function with predefined parameters. The experimental results confirmed the benefit of Sentinels concept and Hypervariance embedded into MBOA algorithm for tracking a moving optimum.