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Application of multiobjective genetic programming to the design of robot failure recognition systems

Zhang, Y. and Rockett, P.I. (2009) Application of multiobjective genetic programming to the design of robot failure recognition systems. IEEE Transactions on Automation Science and Engineering, 6 (2). pp. 372-376. ISSN 1545-5955

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Abstract

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge.

Item Type: Article
Copyright, Publisher and Additional Information: © Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Autonomous robots; failure recognition; feature extraction; multiobjective genetic programming (MOGP)
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 12 May 2009 13:12
Last Modified: 08 Feb 2013 16:58
Published Version: http://dx.doi.org/10.1109/TASE.2008.2004414
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TASE.2008.2004414
URI: http://eprints.whiterose.ac.uk/id/eprint/8566

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