Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming

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

@InProceedings{Mei:2017:EuroGP,

• author = "Yi Mei and Su Nguyen and Mengjie Zhang",
• title = "Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming",
• booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming",
• year = "2017",
• month = "19-21 " # apr,
• editor = "Mauro Castelli and James McDermott and Lukas Sekanina",
• series = "LNCS",
• volume = "10196",
• publisher = "Springer Verlag",
• address = "Amsterdam",
• pages = "147--163",
• organisation = "species",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-319-55695-6",
• DOI = "doi:10.1007/978-3-319-55696-3_10",
• abstract = "Genetic Programming (GP) has achieved success in evolving dispatching rules for job shop scheduling problems, particularly in dynamic environments. However, there is still great potential to improve the performance of GP. One challenge that is yet to be addressed is the huge search space. In this paper, we propose a simple yet effective approach to improve the effectiveness and efficiency of GP. The new approach is based on a newly defined time-invariance property of dispatching rules, which is derived from the idea of translational invariance from machine learning. Then, we develop a new terminal selection scheme to guarantee the time-invariance throughout the GP process. The experimental studies show that by considering the time-invariance, GP can achieve much better rules in a much shorter time.",
• notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017",

}

Genetic Programming entries for Yi Mei Su Nguyen Mengjie Zhang

Citations