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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 |
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| Licence URL(s): | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved |
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|---|---|---|---|---|
| Wu_etal_LNCS_2016.pdf | Fulltext - Published Version | 1.91 MB | Adobe PDF | Under Embargo until 3000-08-31 Request a copy |
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