Model Guided Sampling Optimization for Low-dimensional Problems

Lukáš Bajer, Martin Holeňa

2015

Abstract

Optimization of very expensive black-box functions requires utilization of maximum information gathered by the process of optimization. Model Guided Sampling Optimization (MGSO) forms a more robust alternative to Jones’ Gaussian-process-based EGO algorithm. Instead of EGO’s maximizing expected improvement, the MGSO uses sampling the probability of improvement which is shown to be helpful against trapping in local minima. Further, the MGSO can reach close-to-optimum solutions faster than standard optimization algorithms on low dimensional or smooth problems.

Paper Citation

in Harvard Style

Bajer L. and Holeňa M. (2015). Model Guided Sampling Optimization for Low-dimensional Problems . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-074-1, pages 451-456. DOI: 10.5220/0005222404510456

in Bibtex Style

@conference{icaart15,
author={Lukáš Bajer and Martin Holeňa},
title={Model Guided Sampling Optimization for Low-dimensional Problems},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2015},
pages={451-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005222404510456},
isbn={978-989-758-074-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Model Guided Sampling Optimization for Low-dimensional Problems
SN - 978-989-758-074-1
AU - Bajer L.
AU - Holeňa M.
PY - 2015
SP - 451
EP - 456
DO - 10.5220/0005222404510456