Grammar-Guided Linear Genetic Programming for Dynamic Job Shop Scheduling

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

@InProceedings{Huang:2023:GECCO,

• author = "Zhixing Huang and Yi Mei and Fangfang Zhang and Mengjie Zhang",
• title = "{Grammar-Guided} Linear Genetic Programming for Dynamic Job Shop Scheduling",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "1137--1145",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, linear genetic programming, grammar, dynamic job shop scheduling",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590394",
• size = "9 pages",
• abstract = "Dispatching rules are commonly used to make instant decisions in dynamic scheduling problems. Linear genetic programming (LGP) is one of the effective methods to design dispatching rules automatically. However, the effectiveness and efficiency of LGP methods are limited due to the large search space. Exploring the entire search space of programs is inefficient for LGP since a large number of programs might contain redundant blocks and might be inconsistent with domain knowledge, which would further limit the effectiveness of the produced LGP models. To improve the performance of LGP in dynamic job shop scheduling problems, this paper proposes a grammar-guided LGP to make LGP focus more on promising programs. Our dynamic job shop scheduling simulation results show that the proposed grammar-guided LGP has better training efficiency than basic LGP, and can produce solutions with good explanations. Further analyses show that grammar-guided LGP significantly improves the overall test effectiveness when the number of LGP registers increases.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Zhixing Huang Yi Mei Fangfang Zhang Mengjie Zhang

Citations