Publication detail

Bayesian Optimization Algorithms for Dynamic Problems

KOBLIHA, M. SCHWARZ, J. OČENÁŠEK, J.

Original Title

Bayesian Optimization Algorithms for Dynamic Problems

Type

article in a collection out of WoS and Scopus

Language

English

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 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.

Keywords

BOA algorithm, dynamic problem, optimalization

Authors

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

RIV year

2006

Released

8. 3. 2006

Publisher

Springer Verlag

Location

Budapest

ISBN

3-540-33237-5

Book

Applications of Evolutionary Computing

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

2006

Number

3907

State

Federal Republic of Germany

Pages from

800

Pages to

804

Pages count

5

BibTex

@inproceedings{BUT22416,
  author="Miloš {Kobliha} and Josef {Schwarz} and Jiří {Očenášek}",
  title="Bayesian Optimization Algorithms for Dynamic Problems",
  booktitle="Applications of Evolutionary Computing",
  year="2006",
  journal="Lecture Notes in Computer Science",
  volume="2006",
  number="3907",
  pages="800--804",
  publisher="Springer Verlag",
  address="Budapest",
  isbn="3-540-33237-5",
  issn="0302-9743"
}