Hyper-heuristic Evolution of Dispatching Rules: A Comparison of Rule Representations
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Branke:2015:EC,
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author = "Juergen Branke and Torsten Hildebrandt and
Bernd Scholz-Reiter",
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title = "Hyper-heuristic Evolution of Dispatching Rules: A
Comparison of Rule Representations",
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journal = "Evolutionary Computation",
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year = "2015",
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volume = "23",
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number = "2",
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pages = "249--277",
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month = "Summer",
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keywords = "genetic algorithms, genetic programming, Job Shop
Scheduling, Dispatching Rule, Representation, CMA-ES,
Artificial Neural Network",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/EVCO_a_00131",
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size = "29 pages",
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abstract = "Dispatching rules are frequently used for real-time,
on-line scheduling in complex manufacturing systems.
Design of such rules is usually done by experts in a
time consuming trial-and-error process. Recently,
evolutionary algorithms have been proposed to automate
the design process. There are several possibilities to
represent rules for this hyper-heuristic search.
Because the representation determines the search
neighbourhood and the complexity of the rules that can
be evolved, a suitable choice of representation is key
for a successful evolutionary algorithm. In this paper
we empirically compare three different representations,
both numeric and symbolic, for automated rule design: A
linear combination of attributes, a representation
based on Artificial Neural Networks, and a tree
representation. Using appropriate Evolutionary
Algorithms (CMA-ES for the Neural Network and linear
representations, Genetic Programming for the tree
representation), we empirically investigate the
suitability of each representation in a dynamic
stochastic job shop scenario. We also examine the
robustness of the evolved dispatching rules against
variations in the underlying job shop scenario, and
visualise what the rules do in order to get an
intuitive understanding of their inner workings.
Results indicate that the tree representation using an
improved version of Genetic Programming gives the best
results if many candidate rules can be evaluated,
closely followed by the Neural Network representation
that leads to good results already for small to
moderate computational budgets. The linear
representation is found to be competitive only for
extremely small computational budgets.",
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notes = "Warwick Business School, University of Warwick, CV4
7AL Coventry, UK",
- }
Genetic Programming entries for
Jurgen Branke
Torsten Hildebrandt
Bernd Scholz-Reiter
Citations