@inproceedings{2a65f4cc7882436dabf28c5dee7c0a1a,
title = "Towards single-And multiobjective Bayesian global optimization for mixed integer problems",
abstract = "Bayesian Global Optimization (BGO) is a very efficient technique to optimize expensive evaluation problems. However, the application domain is limited to continuous search spaces when using a BGO algorithm. To solve mixed integer problems with a BGO algorithm, this paper adapts the heterogeneous distance function to construct the Kriging models and applies these new Kriging models in Multi-objective Bayesian Global Optimization (MOBGO). The proposed mixed integer MOBGO algorithm and the traditional MOBGO algorithm are compared on three mixed integer multi-objective optimization problems (MOP), w.r.t. The mean value of the hypervolume (HV) and the related standard deviation.",
author = "Kaifeng Yang and {Van Der Blom}, Koen and Thomas B{\"a}ck and Michael Emmerich",
note = "Publisher Copyright: {\textcopyright} 2019 Author(s).; 14th International Global Optimization Workshop, LeGO 2018 ; Conference date: 18-09-2018 Through 21-09-2018",
year = "2019",
month = feb,
day = "12",
doi = "10.1063/1.5090011",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Deutz, {Andre H.} and Hille, {Sander C.} and Sergeyev, {Yaroslav D.} and Emmerich, {Michael T. M.}",
booktitle = "Proceedings LeGO 2018 � 14th International Global Optimization Workshop",
}