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 PR, Lopez-Ibanez M, Ochoa G, Paechter B (ed.) Parallel Problem Solving from Nature - PPSN XIV, Cham, Switzerland: Springer Int Publishing Ag. International Conference on Parallel Problem Solving from Nature 2016: PPSN XIV, 17.9.2016 - 21.9.2017, Edinburgh, pp. 37-47.
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science, 9921
Conference Name: International Conference on Parallel Problem Solving from Nature 2016: PPSN XIV
Conference Dates: 2016-09-17T00:00:00Z
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: Book Chapter: publisher version
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.
URL: https://link.springer.com/chapter/10.1007/978-3-319-45823-6_4

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