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An Importance Sampling Approach to the Estimation of Algorithm Performance in Automated Algorithm Design Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints

Authors : Ya. D. Sergeyev, D. E. Kvasov, M. S. Mukhametzhanov

Published in: Learning and Intelligent Optimization

Publisher: Springer International Publishing

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Abstract

In this paper, multidimensional test problems for methods solving constrained Lipschitz global optimization problems are proposed. A new class of GKLS-based multidimensional test problems with continuously differentiable multiextremal objective functions and non-linear constraints is described. In these constrained problems, the global minimizer does not coincide with the global minimizer of the respective unconstrained test problem, and is always located on the boundaries of the admissible region. Two types of constraints are introduced. The possibility to choose the difficulty of the admissible region is available.

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previous chapter Accelerating Gradient Descent with Projective Response Surface Methodology
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Metadata
Title
Emmental-Type GKLS-Based Multiextremal Smooth Test Problems with Non-linear Constraints
Authors
Ya. D. Sergeyev
D. E. Kvasov
M. S. Mukhametzhanov
Copyright Year
2017
Book
Learning and Intelligent Optimization

Print ISBN: 978-3-319-69403-0

Electronic ISBN: 978-3-319-69404-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69404-7_35

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