Publication Details
Bayesovské optimalizační algoritmy v dynamickém prostředí
BOA, dynamic problems, evolutionary optimization
This paper is an experimental study investigating the capability of Bayesian optimization algorithms to solve dynamic problems. We tested the performance of two types of Bayesian optimization algorithms - Mixed continuous-discrete Bayesian Optimization Algorithm (MBOA) [1], and Adaptive Mixed Bayesian Optimization Algorithm (AMBOA)[2]. We have compared the behaviour of both algorithms on a simple dynamic environment defined as a time-varying function with predefined parameters. The experimental results confirmed the capability of both BOA algorithms to adapt the search process, but for a limited environment change. The AMBOA with the variance adaptation outperformed the MBOA algorithm.
@inproceedings{BUT22417,
author="Miloš {Kobliha}",
title="Bayesovské optimalizační algoritmy v dynamickém prostředí",
booktitle="Sborník příspevků ze semináře Počítačové Architektury & Diagnostika",
year="2005",
pages="6",
publisher="České vysoké učení technické",
address="Praha",
isbn="80-01-03298-1"
}