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. © Springer International Publishing AG 2016.
Original language | English |
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Title of host publication | Parallel Problem Solving from Nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings |
Editors | Julia HANDL, Emma HART, Peter R. LEWIS, Manuel LÓPEZ-IBÁÑEZ, Gabriela OCHOA, Ben PAECHTER |
Publisher | Springer |
Pages | 37-47 |
Number of pages | 11 |
ISBN (Electronic) | 9783319458236 |
ISBN (Print) | 9783319458229 |
DOIs | |
Publication status | Published - 2016 |
Externally published | Yes |
Event | 14th International Conference on Parallel Problem Solving from Nature - Edinburgh, Scotland, United Kingdom Duration: 17 Sept 2016 → 21 Sept 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 9921 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 14th International Conference on Parallel Problem Solving from Nature |
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Abbreviated title | PPSN 2016 |
Country/Territory | United Kingdom |
City | Scotland |
Period | 17/09/16 → 21/09/16 |
Funding
This paper was partly supported by the National Natural Science Foundation of China under Grant (Grants. 51305024 and 61329302) and EPSRC (Grant No. EP/J017515/1). Xin Yao was supported by a Royal Society Wolfson Research Merit Award.
Keywords
- Adaptive operator selection
- Hyper-heuristics
- Sliding multi-armed bandit
- Software project scheduling