Ni, J. and Rockett, P.I. (2014) Tikhonov Regularization as a Complexity Measure in Multiobjective Genetic Programming. IEEE Transactions on Evolutionary Computation, 19 (2). 157 - 166. ISSN 1089-778X
Abstract
© 1997-2012 IEEE. In this paper, we propose the use of Tikhonov regularization in conjunction with node count as a general complexity measure in multiobjective genetic programming. We demonstrate that employing this general complexity yields mean squared test error measures over a range of regression problems, which are typically superior to those from conventional node count (but never statistically worse). We also analyze the reason that our new method outperforms the conventional complexity measure and conclude that it forms a decision mechanism that balances both syntactic and semantic information.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 IEEE. This is an author-produced version of a paper accepted for publication in IEEE Transactions on Evolutionary Computation. Uploaded in accordance with the publisher's self-archiving policy |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 May 2015 14:47 |
Last Modified: | 29 Mar 2018 04:29 |
Published Version: | https://dx.doi.org/10.1109/TEVC.2014.2306994 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/TEVC.2014.2306994 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:85545 |