NDLI: Surrogate-assisted coevolutionary search

Content Provider	IEEE Xplore Digital Library
Author	YewSoon Ong Keane, A.J. Nair, P.B.
Copyright Year	2002
Description	Author affiliation: Computational Eng. & Design Centre, Southampton Univ., UK (YewSoon Ong; Keane, A.J.; Nair, P.B.)
Abstract	This paper is concerned with an experimental evaluation of coevolutionary optimization techniques, which are integrated with surrogate models of the fitness function. The motivation for this study arises from the fact that since coevolutionary search is based on the divide-and-conquer paradigm, it may be possible to circumvent the curse of dimensionality inherent in surrogate modeling techniques such as radial basis networks. We investigate the applicability of the algorithms presented in this paper to solve computationally expensive optimization problems on a limited computational budget via studies on a benchmark test function and a real world two-dimensional cantilevered space structure design problem. We show that by employing approximate models for the fitness, it becomes possible to converge to good solutions even for functions with a high degree of epistasis.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	1140
Ending Page	1145
File Size	392909
Page Count	6
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1202800
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Convergence Neural networks Design optimization Design engineering Computational efficiency Benchmark testing Algorithm design and analysis Computer networks Degradation Evolutionary computation
Content Type	Text
Resource Type	Article

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