Genetic Programming with Diverse Partner Selection for Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{xu:2022:GECCOcomp2,

• author = "Meng Xu and Yi Mei and Fangfang Zhang and Mengjie Zhang",
• title = "Genetic Programming with Diverse Partner Selection for Dynamic Flexible Job Shop Scheduling",
• booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion",
• year = "2022",
• editor = "Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco {Fernandez de Vega} and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William {La Cava} and Gabriel Kronberger and Steven Gustafson",
• pages = "615--618",
• address = "Boston, USA",
• series = "GECCO '22",
• month = "9-13 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, hyperheuristic, diverse partner selection",
• isbn13 = "978-1-4503-9268-6/22/07",
• DOI = "doi:10.1145/3520304.3528920",
• abstract = "Dynamic flexible job shop scheduling (DFJSS) aims to make decisions for machine assignment and operation sequencing simultaneously to get an effective schedule under dynamic environments. Genetic programming hyper-heuristic (GPHH) has been successfully applied to evolve scheduling heuristics for the DFJSS problem. Parent selection plays an important role in GPHH for generating high-quality offspring. Traditional GPHHs select parents for crossover purely based on fitness (e.g., tournament selection). This might be too greedy to get good offspring and the selected parents might have similar structures/behaviours. In this paper, a GPHH method with a new diverse partner selection (DPS) scheme is proposed, namely GPDPS, for DFJSS. Specifically, we first define a new multi-case fitness to characterise the behaviour of each scheduling heuristic for DFJSS. Then, the newly proposed DPS method selects a pair of complementary high-quality parents for crossover to generate offspring. The experimental results show that GPDPS significantly outperforms the GPHH method on most of the DFJSS scenarios, in terms of both test performance and convergence speed.",
• notes = "GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Meng Xu Yi Mei Fangfang Zhang Mengjie Zhang

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