Rockett, P. orcid.org/0000-0002-4636-7727 (2022) Constant optimization and feature standardization in multiobjective genetic programming. Genetic Programming and Evolvable Machines, 23 (1). pp. 37-69. ISSN 1389-2576
Abstract
This paper extends the numerical tuning of tree constants in genetic programming (GP) to the multiobjective domain. Using ten real-world benchmark regression datasets and employing Bayesian comparison procedures, we first consider the effects of feature standardization (without constant tuning) and conclude that standardization generally produces lower test errors, but, contrary to other recently published work, we find or{blue}{a much less clear trend for} tree sizes. In addition, we consider the effects of constant tuning -- with and without feature standardization -- and observe that i) constant tuning invariably improves test error, and ii) usually decreases tree size. Combined with standardization, constant tuning produces the best test error results; tree sizes, however, are increased. We also examine the effects of applying constant tuning only once at the end a conventional GP run which turns out to be surprisingly promising. Finally, we consider the merits of using numerical procedures to tune tree constants and observe that for around half the datasets evolutionary search alone is superior whereas for the remaining half, parameter tuning is superior. We identify a number of open research questions that arise from this work.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | Multiobjective genetic programming; Constant optimization; Feature standardization; Bayesian testing |
Dates: |
|
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: | 05 Aug 2021 09:36 |
Last Modified: | 23 Jun 2022 09:15 |
Status: | Published |
Publisher: | Springer Nature |
Refereed: | Yes |
Identification Number: | 10.1007/s10710-021-09410-y |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:176445 |
Download
Filename: Rockett2021_Article_ConstantOptimizationAndFeature.pdf
Licence: CC-BY 4.0