NDLI: Surrogate based EA for expensive optimization problems

Content Provider	IEEE Xplore Digital Library
Author	Bhattacharya, M.
Copyright Year	2007
Description	Author affiliation: Charles Sturt Univ., Thurgoona (Bhattacharya, M.)
Abstract	Real life optimization problems often require finding optimal solution to complex high dimensional, multimodal problems involving computationally very expensive fitness function evaluations. Use of a population based iterative technique like evolutionary algorithms in such problem domains is thus practically prohibitive. An attractive alternative is to build surrogates or use an approximation of the actual fitness functions to be evaluated. Many regression and interpolation tools are available to build such meta models. This paper briefly discusses the architectures and use of such meta-modeling tools in an evolutionary optimization context. We further present an evolutionary algorithm framework which involves use of surrogate models for fitness function evaluation. The original framework namely, the dynamic approximate fitness based hybrid EA (DAFHEA) model [14] reduces computation time by controlled use of meta-models (in this case approximate model generated by support vector machine regression) to partially replace the actual function evaluation by approximate function evaluation. However, the underlying assumption in DAFHEA is that the training samples for the meta-model are generated from a single uniform model. This does not take into account uncertain scenarios involving noisy fitness functions. The enhanced model, DAFHEA-II, incorporates a multiple-model based learning approach for the support vector machine approximator to counter effects of noise [15]. Empirical results obtained by evaluating the frameworks using several benchmark functions (both non- noisy and noisy versions) are presented.
Starting Page	3847
Ending Page	3854
File Size	298804
Page Count	8
File Format	PDF
ISBN	9781424413393
DOI	10.1109/CEC.2007.4424972
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-25
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Predictive models Evolutionary computation Least squares approximation Optimal control Artificial neural networks Response surface methodology Approximation algorithms Genetic mutations Training data Robust control
Content Type	Text
Resource Type	Article

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