Multi-Objective Genetic Programming Based on Decomposition on Evolving Scheduling Heuristics for Dynamic Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Xu:2023:GECCOcomp,
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author = "Meng Xu and Yi Mei and Fangfang Zhang and
Mengjie Zhang",
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title = "Multi-Objective Genetic Programming Based on
Decomposition on Evolving Scheduling Heuristics for
Dynamic Scheduling",
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booktitle = "Proceedings of the Companion Conference on Genetic and
Evolutionary Computation",
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year = "2023",
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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",
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pages = "427--430",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming,
decomposition, MOEA/D, multi-objective dynamic flexible
job shop scheduling: Poster",
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isbn13 = "9798400701207",
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DOI = "doi:10.1145/3583133.3590582",
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size = "4 pages",
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abstract = "Dynamic flexible job shop scheduling (DFJSS) is an
important combinatorial optimisation problem that
requires handling machine assignment and operation
sequencing simultaneously in dynamic environments.
Genetic programming (GP) has achieved great success to
evolve scheduling heuristics for DFJSS. In
manufacturing, multi-objective DFJSS (MO-DFJSS) is more
common and challenging due to conflicting objectives.
Existing Pareto dominance-based multi-objective GP
methods show their limitations of not providing good
spreadability and consistency in heuristic behaviour.
Multi-objective evolutionary algorithm based on
decomposition (MOEA/D) has the potential to provide
good spreadability and consistency due to the
mechanisms of weights-based subproblems decomposition
and neighbours-based evolution. However, it is
non-trivial to apply MOEA/D to MO-DFJSS since we need
to search in heuristic space. To address these
challenges, we propose a multi-objective GP approach
based on decomposition (MOGP/D) that incorporates the
advantages of MOEA/D and GP to learn scheduling
heuristics for MO-DFJSS. A mapping strategy is designed
to find the fittest individual for each subproblem.
Extensive experiments show that MOGP/D obtains
competitive performance with the state-of-the-art
methods for MO-DFJSS, and good spreadability and
consistency in heuristic behaviour.",
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notes = "Also known as \cite{xu:2023:GECCOcomp} 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
Meng Xu
Yi Mei
Fangfang Zhang
Mengjie Zhang
Citations