Rockett, P. orcid.org/0000-0002-4636-7727, Kaszubowski Lopes, Y., Dou, T. et al. (1 more author) (2019) d(Tree)-by-dx : automatic and exact differentiation of genetic programming trees. In: Pérez García, H., González, L.S., Limas, M.C., Pardo, H.Q. and Rodríguez, E.C., (eds.) Hybrid Artificial Intelligent Systems. 14th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2019), 04-06 Sep 2019, León, Spain. Lecture Notes in Artificial Intellgence (11734). Springer , León, Spain , pp. 133-144. ISBN 9783030298586
Abstract
Genetic programming (GP) has developed to the point where it is a credible candidate for the `black box' modeling of real systems. Wider application, however, could greatly benefit from its seamless embedding in conventional optimization schemes, which are most efficiently carried out using gradient-based methods. This paper describes the development of a method to automatically differentiate GP trees using a series of tree transformation rules; the resulting method can be applied an unlimited number of times to obtain higher derivatives of the function approximated by the original, trained GP tree. We demonstrate the utility of our method using a number of illustrative gradient-based optimizations that embed GP models.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2019 Springer Nature. This is an author-produced version of a paper subsequently published in HAIS 2019 Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Genetic programming; Automatic differentiation; Optimization; Real-world applications |
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) |
Funding Information: | Funder Grant number ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/N022351/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 21 Jun 2019 11:06 |
Last Modified: | 26 Aug 2020 00:38 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Artificial Intellgence |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-030-29859-3_12 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:147647 |