Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Zhu:2023:GECCO,

• author = "Luyao Zhu and Fangfang Zhang and Xiaodong Zhu and Ke Chen2 and Mengjie Zhang",
• title = "Sample-Aware Surrogate-Assisted Genetic Programming for Scheduling Heuristics Learning in Dynamic Flexible 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 = "384--392",
• 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, automated scheduling heuristics design, dynamic flexible job shop scheduling, surrogate samples",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583131.3590440",
• size = "9 pages",
• abstract = "Genetic programming (GP) has been successfully introduced to learn scheduling heuristics for dynamic flexible job shop scheduling (DFJSS) automatically. However, the evaluations of GP individuals are normally time-consuming, especially with long DFJSS simulations. Taking k-nearest neighbour with phenotypic characterisations of GP individuals as a surrogate approach, has been successfully used to preselect GP offspring to the next generation for effectiveness improvement. However, this approach is not straightforward to improve the training efficiency, which is normally the primary goal of surrogate. In addition, there is no study on which GP individuals (samples) are good for building surrogate models. To this end, first, this paper proposes a surrogate-assisted GP algorithm to reduce the training time of learning scheduling heuristics for DFJSS. Second, this paper further proposes an effective sampling strategy for surrogate-assisted GP. The results show that our proposed algorithm can achieve comparable performance with only about a third of training time of traditional GP. With the same training time, the proposed algorithm can significantly improve the quality of learned scheduling heuristics in all examined scenarios. Furthermore, the evolved scheduling heuristics by the proposed sample-aware surrogate-assisted GP are more interpretable with smaller rule sizes than traditional GP.",
• 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 Luyao Zhu Fangfang Zhang Xiaodong Zhu Ke Chen2 Mengjie Zhang

Citations