- author = "Gabriel Duflo and Emmanuel Kieffer and Matthias R. Brust and Gregoire Danoy and Pascal Bouvry",
- booktitle = "2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)",
- title = "A {GP} Hyper-Heuristic Approach for Generating {TSP} Heuristics",
- year = "2019",
- pages = "521--529",
- month = may,
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/IPDPSW.2019.00094",
- abstract = "A wide range of heuristics has been developed over the last decades as a way to obtain good quality solutions in reasonable time on large scale optimisation problems. However, heuristics are problem specific, i.e. lack of generalisation potential, while requiring time to design. Hyper-heuristics have been proposed to address these limitations by directly searching in the heuristics' space. This work more precisely focuses on a heuristic generation method, as opposed to heuristic selection, for the traveling salesman problem (TSP). Learning is achieved with a genetic programming (GP) approach, for which novel specific terminals are introduced. The performance of the proposed GP hyper-heuristic is evaluated on a large set of TSP instances and compared to state-of-the-art heuristics. Experiments demonstrate that the generated heuristics are outperforming existing ones while having similar or lower complexity.",
-
notes = "Interdisciplinary Centre for Security, Reliability and
Trust (SnT), University of Luxembourg,
Esch-sur-Alzette, Luxembourg
Also known as \cite{8778254}",
