Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels
This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of G...
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Published in: | IEEE transactions on evolutionary computation Vol. 10; no. 4; pp. 421 - 439 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
New York
IEEE
01-08-2006
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects: | |
Online Access: | Get full text |
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Summary: | This paper presents and analyzes in detail an efficient search method based on evolutionary algorithms (EA) assisted by local Gaussian random field metamodels (GRFM). It is created for the use in optimization problems with one (or many) computationally expensive evaluation function(s). The role of GRFM is to predict objective function values for new candidate solutions by exploiting information recorded during previous evaluations. Moreover, GRFM are able to provide estimates of the confidence of their predictions. Predictions and their confidence intervals predicted by GRFM are used by the metamodel assisted EA. It selects the promising members in each generation and carries out exact, costly evaluations only for them. The extensive use of the uncertainty information of predictions for screening the candidate solutions makes it possible to significantly reduce the computational cost of singleand multiobjective EA. This is adequately demonstrated in this paper by means of mathematical test cases and a multipoint airfoil design in aerodynamics |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 1089-778X 1941-0026 |
DOI: | 10.1109/TEVC.2005.859463 |