Multi-objective integer programming approaches for solving optimal feature selection problem a new perspective on multi-objective optimization problems in SBSE
The optimal feature selection problem in software product line is typically addressed by the approaches based on Indicator-based Evolutionary Algorithm (IBEA). In this study we first expose the mathematical nature of this problem --- multi-objective binary integer linear programming. Then, we implem...
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| Vydáno v: | 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE) s. 1231 - 1242 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
New York, NY, USA
ACM
27.05.2018
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| Edice: | ACM Conferences |
| Témata: | |
| ISBN: | 9781450356381, 1450356389 |
| ISSN: | 1558-1225 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | The optimal feature selection problem in software product line is typically addressed by the approaches based on Indicator-based Evolutionary Algorithm (IBEA). In this study we first expose the mathematical nature of this problem --- multi-objective binary integer linear programming. Then, we implement/propose three mathematical programming approaches to solve this problem at different scales. For small-scale problems (roughly less than 100 features), we implement two established approaches to find all exact solutions. For medium-to-large problems (roughly, more than 100 features), we propose one efficient approach that can generate a representation of the entire Pareto front in linear time complexity. The empirical results show that our proposed method can find significantly more non-dominated solutions in similar or less execution time, in comparison with IBEA and its recent enhancement (i.e., IBED that combines IBEA and Differential Evolution). |
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| ISBN: | 9781450356381 1450356389 |
| ISSN: | 1558-1225 |
| DOI: | 10.1145/3180155.3180257 |