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GECCO
2007
Springer

A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation

9 years 5 months ago
A study on metamodeling techniques, ensembles, and multi-surrogates in evolutionary computation
Surrogate-Assisted Memetic Algorithm(SAMA) is a hybrid evolutionary algorithm, particularly a memetic algorithm that employs surrogate models in the optimization search. Since most of the objective function evaluations in SAMA are approximated, the search performance of SAMA is likely to be affected by the characteristics of the models used. In this paper, we study the search performance of using different metamodeling techniques, ensembles, and multisurrogates in SAMA. In particular, we consider the SAMATRF, a SAMA model management framework that incorporates a trust region scheme for interleaving use of exact objective function with computationally cheap local metamodels during local searches. Four different metamodels, namely Gaussian Process (GP), Radial Basis Function (RBF), Polynomial Regression (PR), and Extreme Learning Machine (ELM) neural network are used in the study. Empirical results obtained show that while some metamodeling techniques perform best on particular bench...
Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendh
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff
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