Authors:
Naoya Takimoto
and
Hiroshi Morita
Affiliation:
Osaka University, Japan
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.