Genetic Programming Hyper-heuristic with Gaussian Process-based Reference Point Adaption for Many-Objective Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Masood:2022:CEC,
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author = "Atiya Masood and Gang Chen2 and Yi Mei and
Harith Al-Sahaf and Mengjie Zhang",
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booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Genetic Programming Hyper-heuristic with Gaussian
Process-based Reference Point Adaption for
Many-Objective Job Shop Scheduling",
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year = "2022",
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editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
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address = "Padua, Italy",
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month = "18-23 " # jul,
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isbn13 = "978-1-6654-6708-7",
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abstract = "Job Shop Scheduling (JSS) is an important real-world
problem. However, the problem is challenging because of
many conflicting objectives and the complexity of
production flows. Genetic programming-based
hyper-heuristic (GP-HH) is a useful approach for
automatically evolving effective dispatching rules for
many-objective JSS. However, the evolved Pareto-front
is highly irregular, seriously affecting the
effectiveness of GP-HH. Although the reference points
method is one of the most prominent and efficient
methods for diversity maintenance in many-objective
problems, it usually uses a uniform distribution of
reference points which is only appropriate for a
regular Pareto-front. In fact, some reference points
may never be linked to any Pareto-optimal solutions,
rendering them useless. These useless reference points
can significantly impact the performance of any
reference-point-based many-objective optimization
algorithms such as NSGA-III. This paper proposes a new
reference point adaption process that explicitly
constructs the distribution model using Gaussian
process to effectively reduce the number of useless
reference points to a low level, enabling a close match
between reference points and the distribution of
Pareto-optimal solutions. We incorporate this mechanism
into NSGA-III to build a new algorithm called
MARP-NSGA-III which is compared experimentally to
several popular many-objective algorithms. Experiment
results on a large collection of many-objective
benchmark JSS instances clearly show that MARP-NSGA-III
can significantly improve the performance by using our
Gaussian Process-based reference point adaptation
mechanism.",
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keywords = "genetic algorithms, genetic programming, Adaptation
models, Job shop scheduling, Processor scheduling,
Sociology, Gaussian processes, Production, Maintenance
engineering, Many-objective Optimization, Evolutionary
Computation, Gaussian Process, Adaptive reference
points, Job Shop Scheduling",
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DOI = "doi:10.1109/CEC55065.2022.9870322",
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notes = "Also known as \cite{9870322}",
- }
Genetic Programming entries for
Atiya Masood
Aaron Chen
Yi Mei
Harith Al-Sahaf
Mengjie Zhang
Citations