Publication detail

Bayesian Optimization Algorithm in Dynamic Environment

KOBLIHA, M., SCHWARZ, J., OČENÁŠEK, J.

Original Title

Bayesian Optimization Algorithm in Dynamic Environment

English Title

Bayesian Optimization Algorithm in Dynamic Environment

Type

conference paper

Language

en

Original Abstract

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), and Adaptive Mixed Bayesian Optimization Algorithm (AMBOA). We have compared the behaviour of both algorithms on a simple dynamic  environment defined as a time-varying function with predefined parameters.

English abstract

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), and Adaptive Mixed Bayesian Optimization Algorithm (AMBOA). We have compared the behaviour of both algorithms on a simple dynamic  environment defined as a time-varying function with predefined parameters.

Keywords

Dynamic environment, Estimation of Distribution Algorithms, Bayesian Optimization Algorithms, variance adaptation, time-varying test function

RIV year

2005

Released

15.06.2005

Publisher

Faculty of Mechanical Engineering BUT

Location

Brno, CZ

ISBN

80-214-2961-5

Book

Mendel 2005 11th Internacional Conference on Soft Computing

Pages from

15

Pages to

20

Pages count

6

BibTex


@inproceedings{BUT21527,
  author="Miloš {Kobliha} and Josef {Schwarz} and Jiří {Očenášek}",
  title="Bayesian Optimization Algorithm in Dynamic Environment",
  annote="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), and Adaptive Mixed Bayesian Optimization Algorithm (AMBOA). We have compared the behaviour of both algorithms on a simple dynamic  environment defined as a time-varying function with predefined parameters.",
  address="Faculty of Mechanical Engineering BUT",
  booktitle="Mendel 2005 11th Internacional Conference on Soft Computing",
  chapter="21527",
  institution="Faculty of Mechanical Engineering BUT",
  year="2005",
  month="june",
  pages="15--20",
  publisher="Faculty of Mechanical Engineering BUT",
  type="conference paper"
}