Detail publikace

Bayesian Optimization Algorithms for Dynamic Problems

KOBLIHA, M. SCHWARZ, J. OČENÁŠEK, J.

Originální název

Bayesian Optimization Algorithms for Dynamic Problems

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

BOA algorithm, dynamic problem, optimalization

Autoři

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

Rok RIV

2006

Vydáno

8. 3. 2006

Nakladatel

Springer Verlag

Místo

Budapest

ISBN

3-540-33237-5

Kniha

Applications of Evolutionary Computing

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

2006

Číslo

3907

Stát

Spolková republika Německo

Strany od

800

Strany do

804

Strany počet

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"
}