Stochastic and deterministic algorithms for continuous black-box optimization

Leiden Repository

Stochastic and deterministic algorithms for continuous black-box optimization

Type: Doctoral Thesis
Title: Stochastic and deterministic algorithms for continuous black-box optimization
Author: Wang, H.
Issue Date: 2018-11-01
Keywords: Stochastic optimization
Acquisition function
Orthogonalization
Gaussian process regression
Efficient global optimization
Parallelization
Hypervolume indicator
Abstract: Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is essentially about the efficiency: how to obtain the solution with same quality using as minimal resources (e.g., CPU time or memory usage) as possible? In this thesis, the number of function evaluations is considered as the most important resource to save. To achieve this goal, various efforts have been implemented and applied successfully. One research stream focuses on the so-called stochastic variation (mutation) operator, which conducts an (local) exploration of the search space. The efficiency of those operator has been investigated closely, which shows a good stochastic variation should be able to generate a good coverage of the local neighbourhood around the current search solution. This thesis contributes on this issue by formulating a novel stochastic variation that yields good space coverage.
Promotor: Supervisor: Bäck T.H.W. Co-Supervisor: Emmerich M.T.M.
Faculty: Faculty of Science
University: Leiden University
Handle: http://hdl.handle.net/1887/66671
 

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application/pdf Title Pages_Abstract_Contents 367.7Kb View/Open
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application/pdf Appendices_Bibliography_Index 591.3Kb View/Open
application/pdf Summary in English 205.0Kb View/Open
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application/pdf Curriculum Vitae 344.3Kb View/Open
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