A Multiform Many-Objective Genetic Programming Method for Dynamic Flexible Job Shop Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8464

@InProceedings{pang:2025:GECCO,

• author = "Junwei Pang and Yi Mei and Mengjie Zhang",
• title = "A Multiform Many-Objective Genetic Programming Method for Dynamic Flexible Job Shop Scheduling",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Sarah L. Thomson and Yi Mei",
• pages = "267--276",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, dynamic flexible job shop scheduling, many-objective optimisation, multiform optimisation, Evolutionary Combinatorial Optimization, Metaheuristics",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726371",
• DOI = "doi:10.1145/3712256.3726371",
• size = "10 pages",
• abstract = "Genetic programming-based hyper-heuristic approaches have successfully evolved effective scheduling heuristics for dynamic flexible job shop scheduling. However, in addition to effectiveness, users may prefer other important factors such as model size (i.e., bloat control), structural complexity, and interpretability. To evolve scheduling heuristics considering a wide range of factors, we aim to solve a new many-objective optimisation problem with one effectiveness indicator and four commonly considered model structural complexity measures. To solve this problem, we design a new multiform many-objective genetic programming-based hyper-heuristic algorithm, which optimises this proposed many-objective optimisation task and a constructed single-objective auxiliary task in a multitask manner. This auxiliary task is specifically designed to optimise effectiveness, aiming to find effective individuals and provide beneficial genetic materials for the original task to improve search performance via knowledge transfer. The experimental results show that this approach can produce scheduling heuristics that approximate the Pareto front better than the compared state-of-the-art algorithms across a series of scenarios. Further analysis demonstrates the interpretability of evolved scheduling heuristics and the advantages of considering comprehensive structural complexity measures simultaneously.",
• notes = "GECCO-2025 ECOM A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Junwei Pang Yi Mei Mengjie Zhang

Citations