• No results found

Cover Page The handle http://hdl.handle.net/1887/87271

N/A
N/A
Protected

Academic year: 2021

Share "Cover Page The handle http://hdl.handle.net/1887/87271"

Copied!
2
0
0

Bezig met laden.... (Bekijk nu de volledige tekst)

Hele tekst

(1)

Cover Page

The handle http://hdl.handle.net/1887/87271 holds various files of this Leiden University dissertation.

Author: Bagheri, S.

Title: Self-adjusting surrogate-assisted optimization techniques for expensive constrained black box problems

(2)

Self-A djusting Surrogate-Assisted Optimization T ec hniques for Exp ensiv e Constrained Blac k Bo x Proble ms

Self-Adjusting Surrogate-Assisted

Optimization Techniques for Expensive

Constrained Black Box Problems

Samineh Bagheri

● ● ● ● ● ● ● ●

Leiden University

Figure on the front: Augmented radial basis function interpolation.

Linear combination of weighted cubic radial basis functions and a polynomial tail tting a curve to the evaluated points.

Figure on the back: Gaussian process modeling. Posterior function distribution given the evaluated points using a Gaussian kernel.

●

● ●

●

Referenties

GERELATEERDE DOCUMENTEN

in black-box COPs we do not have access to the feasible subspace formed by the equality constraints, the refine step tries to move the best found solution in each iteration towards

This algorithm suggests to maximize the multiplication of the expected improvement of the objective function and the probability of feasibility, which are both statistical

Although a category (c) transformation provides the possibility for the SOCU optimizer to explore the area close to the feasible subspace with a constant feasibility margin ,

Kriging has the big advantage of providing uncertainty information for surrogates, which is necessary for determining EI. But Kriging – at least in most currently

What we show here for RBF surrogate models holds the same way for GP (or Kriging) surrogate models often used in EGO [90]: Problems with a high condition number have a much

However, the fact that in most cases real-world COPs have objective and constraint functions of different types and nature, motivated us to develop an online model selection

The black point shows the global optimum of the fitness function which is different from the optimum of the constrained problem shown as the gold star.. where n is the size of

SACOBRA, standing for self-adjusting constrained optimization by radial basis function interpolation, is an efficient technique using RBF interpolations as surro- gates for