Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/26225
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Peer Review Status: Refereed
Author(s): Wu, Xiuli
Consoli, Pietro
Minku, Leandro
Ochoa, Gabriela
Yao, Xin
Contact Email: gabriela.ochoa@cs.stir.ac.uk
Title: An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem
Editor(s): Handl, J
Hart, E
Lewis, PR
Lopez-Ibanez, M
Ochoa, G
Paechter, B
Citation: Wu X, Consoli P, Minku L, Ochoa G & Yao X (2016) An Evolutionary Hyper-heuristic for the Software Project Scheduling Problem. In: Handl J, Hart E, Lewis P, Lopez-Ibanez M, Ochoa G & Paechter B (eds.) Parallel Problem Solving from Nature - PPSN XIV. Lecture Notes in Computer Science, 9921. International Conference on Parallel Problem Solving from Nature 2016: PPSN XIV, Edinburgh, 17.09.2016-21.09.2017. Cham, Switzerland: Springer Verlag, pp. 37-47. https://doi.org/10.1007/978-3-319-45823-6_4
Issue Date: 2016
Date Deposited: 29-Nov-2017
Series/Report no.: Lecture Notes in Computer Science, 9921
Conference Name: International Conference on Parallel Problem Solving from Nature 2016: PPSN XIV
Conference Dates: 2016-09-17 - 2017-09-21
Conference Location: Edinburgh
Abstract: Software project scheduling plays an important role in reducing the cost and duration of software projects. It is an NP-hard combinatorial optimization problem that has been addressed based on single and multi-objective algorithms. However, such algorithms have always used fixed genetic operators, and it is unclear which operators would be more appropriate across the search process. In this paper, we propose an evolutionary hyper-heuristic to solve the software project scheduling problem. Our novelties include the following: (1) this is the first work to adopt an evolutionary hyper-heuristic for the software project scheduling problem; (2) this is the first work for adaptive selection of both crossover and mutation operators; (3) we design different credit assignment methods for mutation and crossover; and (4) we use a sliding multi-armed bandit strategy to adaptively choose both crossover and mutation operators. The experimental results show that the proposed algorithm can solve the software project scheduling problem effectively.
Status: VoR - Version of Record
Rights: The publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.
Licence URL(s): http://www.rioxx.net/licenses/under-embargo-all-rights-reserved

Files in This Item:
File Description SizeFormat 
Wu_etal_LNCS_2016.pdfFulltext - Published Version1.91 MBAdobe PDFUnder Embargo until 3000-08-31    Request a copy

Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

The metadata of the records in the Repository are available under the CC0 public domain dedication: No Rights Reserved https://creativecommons.org/publicdomain/zero/1.0/

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.