Multi-Objective Genetic-Programming Hyper-Heuristic for Evolving Interpretable Flexible Job Shop Scheduling Rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{pang:2024:CEC,
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author = "Junwei Pang and Yi Mei and Mengjie Zhang",
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title = "Multi-Objective Genetic-Programming Hyper-Heuristic
for Evolving Interpretable Flexible Job Shop Scheduling
Rules",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Schedules,
Job shop scheduling, Heuristic algorithms, Semantics,
Size measurement, Dispatching, multi-objective genetic
programming, interpretability, flexible job shop
scheduling",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612158",
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abstract = "The job shop scheduling problem is an important
combinatorial optimisation problem in the real world.
Genetic programming hyper-heuristic has been
successfully applied to automatically evolve effective
dispatching rules to make a schedule in real time
without much domain knowledge. However, the
interpretability of GP-evolved rules has been largely
neglected, which could lead to the lack of reliability
and trustworthiness of the evolved rules in practice.
Current work related to interpretable genetic
programming algorithms primarily uses the model size as
the interpretability metric. This could not fully
reflect the interpretability of evolved rules. To
overcome the limitation, we employ structural
complexity and dimension gap as more comprehensive
interpretability measures. In addition, a new
multi-objective genetic programming algorithm, which
applies the a non-dominated sorting method to solve the
objective selection bias issue, is proposed to optimise
the makespan (scheduling objective), structural
complexity and dimension gap simultaneously. A variety
of experiments demonstrates the competitive performance
of our proposed algorithm based on effectiveness,
convergence and diversity. Furthermore, the semantics
of evolved dispatching rules are analysed to show their
better interpretability.",
-
notes = "also known as \cite{10612158}
WCCI 2024",
- }
Genetic Programming entries for
Junwei Pang
Yi Mei
Mengjie Zhang
Citations