DocumentCode :
2699336
Title :
A study of surrogate models for their use in multiobjective evolutionary algorithms
Author :
Montemayor-García, Gerardo ; Toscano-Pulido, Gregorio
Author_Institution :
Inf. Technol. Lab., CINVESTAV - Tamaulipas, Ciudad Victoria, Mexico
fYear :
2011
fDate :
26-28 Oct. 2011
Firstpage :
1
Lastpage :
6
Abstract :
Evolutionary Algorithms (EAs) are bioinspired meta-heuristics that have been successfully used to solve multiobjective optimization problems (MOPs). However, when EAs need to perform several objective function evaluations in order to reach a subobtimal solution and each of these evaluations are computationally expensive, then, these problems can remain intractable even by these meta-heuristics. Therefore, it is necessary to employ an additional strategy in order to reduce the response time of EAs when optimizing these expensive problems. Replacing the original problem with a surrogate model has been an usual strategy for time reduction. However, despite its success, few comparison among surrogate models for multiobjective optimization problems have been reported in the specialized literature. In this paper, we compare four meta-modeling techniques: Radial Basis Functions, Support Vector Regression, Polynomial Regression and Kriging-DACE in different aspects such as accuracy, robustness, efficiency, and scalability with the aim to identify advantages and drawbacks of each meta-modeling technique in order to choose the most suitable one to be combined with multiobjective evolutionary algorithms.
Keywords :
evolutionary computation; optimisation; polynomials; radial basis function networks; regression analysis; support vector machines; Kriging-DACE; bioinspired meta-heuristics; meta-modeling techniques; multiobjective evolutionary algorithms; multiobjective optimization problems; objective function evaluations; polynomial regression; radial basis functions; support vector regression; surrogate models; Accuracy; Computational modeling; Evolutionary computation; Optimization; Robustness; Support vector machines; Training; Multiobjective Evolutionary Algorithms; Multiobjective Problems; Surrogated Models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering Computing Science and Automatic Control (CCE), 2011 8th International Conference on
Conference_Location :
Merida City
Print_ISBN :
978-1-4577-1011-7
Type :
conf
DOI :
10.1109/ICEEE.2011.6106655
Filename :
6106655
Link To Document :
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