Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Park:2016:GECCOcomp,

• author = "John Park and Yi Mei and Gang Chen2 and Mengjie Zhang",
• title = "Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics",
• booktitle = "GECCO '16 Companion: Proceedings of the Companion Publication of the 2016 Annual Conference on Genetic and Evolutionary Computation",
• year = "2016",
• editor = "Tobias Friedrich and Frank Neumann and Andrew M. Sutton and Martin Middendorf and Xiaodong Li and Emma Hart and Mengjie Zhang and Youhei Akimoto and Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and Daniele Loiacono and Julian Togelius and Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and Faustino Gomez and Carlos M. Fonseca and Heike Trautmann and Alberto Moraglio and William F. Punch and Krzysztof Krawiec and Zdenek Vasicek and Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and Boris Naujoks and Enrique Alba and Gabriela Ochoa and Simon Poulding and Dirk Sudholt and Timo Koetzing",
• pages = "109--110",
• keywords = "genetic algorithms, genetic programming: Poster",
• month = "20-24 " # jul,
• organisation = "SIGEVO",
• address = "Denver, USA",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• isbn13 = "978-1-4503-4323-7",
• DOI = "doi:10.1145/2908961.2908985",
• abstract = "investigates the application of fitness sharing to a coevolutionary genetic programming based hyper-heuristic (GP-HH) approach to a dynamic job shop scheduling (DJSS) problem that evolves an ensemble of dispatching rules. Evolving ensembles using GP-HH for DJSS problem is a relatively unexplored area, and has been shown to outperform standard GP-HH procedures that evolve single rules. As a fitness sharing algorithm has not been applied to the specific GP-HH approach, we investigate four different phenotypic distance measures as part of a fitness sharing algorithm. The fitness sharing algorithm may potentially improve the diversity of the constituent members of the ensemble and improve the quality of the ensembles. The results show that the niched co-evolutionary GP approaches evolve smaller sized rules than the base coevolutionary GP approaches, but have similar performances.",
• notes = "Distributed at GECCO-2016.",

}

Genetic Programming entries for John Park Yi Mei Aaron Chen Mengjie Zhang

Citations