- author = "Emmanuel Kieffer and Gregoire Danoy and Matthias R. Brust and Pascal Bouvry and Anass Nagih",
- title = "Tackling Large-Scale and Combinatorial Bi-level Problems with a Genetic Programming Hyper-heuristic",
- journal = "IEEE Transactions on Evolutionary Computation",
- year = "2020",
- volume = "24",
- number = "1",
- pages = "44--56",
- month = feb,
- keywords = "genetic algorithms, genetic programming, bi-level optimization, hyper-heuristics, pricing in the cloud, Stackelberg games",
- ISSN = "1089-778X",
-
URL = "
http://hdl.handle.net/10993/39737",
-
DOI = "
10.1109/TEVC.2019.2906581",
- size = "13 pages",
- abstract = "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.",
-
notes = "University of Luxembourg, Esch-sur-Alzette, 4365
Luxembourg.
also known as \cite{8671761}",
