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Authors: Naoya Takimoto and Hiroshi Morita

Affiliation: Osaka University, Japan

ISBN: 978-989-758-120-5

Keyword(s): Global Optimization, Black-box Function, Bayesian Global Optimization, Kriging, Random Function, Response Surface, Stochastic Process.

Related Ontology Subjects/Areas/Topics: Computer Simulation Techniques ; Formal Methods ; Optimization Issues ; Simulation and Modeling ; Simulation Tools and Platforms ; Stochastic Modeling and Simulation

Abstract: Computer experiments are black-box functions that are expensive to evaluate. One solution to expensive black-box optimization is Bayesian optimization with Gaussian processes. This approach is popularly used in this challenge, and it is efficient when the number of evaluations is limited by cost and time constraints, which is generally true in practice. This paper discusses an optimization method with two acquisition functions. Our new method improves the efficiency of global optimization when the number of evaluations is strictly limited.

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Paper citation in several formats:
Takimoto, N. and Morita, H. (2015). Global Optimization with Gaussian Regression Under the Finite Number of Evaluation.In Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-120-5, pages 192-198. DOI: 10.5220/0005559701920198

@conference{simultech15,
author={Naoya Takimoto. and Hiroshi Morita.},
title={Global Optimization with Gaussian Regression Under the Finite Number of Evaluation},
booktitle={Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2015},
pages={192-198},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005559701920198},
isbn={978-989-758-120-5},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Global Optimization with Gaussian Regression Under the Finite Number of Evaluation
SN - 978-989-758-120-5
AU - Takimoto, N.
AU - Morita, H.
PY - 2015
SP - 192
EP - 198
DO - 10.5220/0005559701920198

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