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Authors: Lukáš Bajer 1 and Martin Holeňa 2

Affiliations: 1 Academy of Sciences of the Czech Republic and Charles University in Prague, Czech Republic ; 2 Academy of Sciences of the Czech Republic, Czech Republic

ISBN: 978-989-758-074-1

Keyword(s): black-box Optimization, Gaussian Process, Surrogate Modelling, EGO.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Enterprise Information Systems ; Evolutionary Computing ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Model-Based Reasoning ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:
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 1: ICAART, ISBN 978-989-758-074-1, pages 451-456. DOI: 10.5220/0005222404510456

@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 1: ICAART,},
year={2015},
pages={451-456},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005222404510456},
isbn={978-989-758-074-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: 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

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