Reference : Tackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming H...
Scientific journals : Article
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/39737
Tackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming Hyper-heuristic
English
Kieffer, Emmanuel mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Danoy, Grégoire mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Computer Science and Communications Research Unit (CSC) >]
Bouvry, Pascal mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Nagih, Anass mailto []
20-Mar-2019
IEEE Transactions on Evolutionary Computation
Yes
International
1089-778X
1941-0026
[en] Bi-level optimization ; Genetic programming ; Hyper-heuristics
[en] Combinatorial bi-level optimization remains a challenging topic, especially when the lower-level is a NP-hard problem. In this work, we tackle large-scale and combinatorial bi-level problems using GP Hyper-heuristics, i.e., an approach that permits to train heuristics like a machine learning model. Our contribution aims at targeting the intensive and complex lower-level optimizations that occur when solving a large-scale and combinatorial bi-level problem. For this purpose, we consider hyper-heuristics through heuristic generation. Using a GP hyper-heuristic approach, we train greedy heuristics in order to make them more reliable when encountering unseen lower-level instances that could be generated during bi-level optimization. To validate our approach referred to as GA+AGH, we tackle instances from the Bi-level Cloud Pricing Optimization Problem (BCPOP) that model the trading interactions between a cloud service provider and cloud service customers. Numerical results demonstrate the abilities of the trained heuristics to cope with the inherent nested structure that makes bi-level optimization problems so hard. Furthermore, it has been shown that training heuristics for lower-level optimization permits to outperform human-based heuristics and metaheuristics which constitute an excellent outcome for bi-level optimization.
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/39737

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