A Cooperative Coevolutionary Approach to Designing Acceptance Tests for Jobs With Weakly Hard Real-Time Constraints
Created by W.Langdon from
gp-bibliography.bib Revision:1.8120
- @InProceedings{salamun:2024:CEC,
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author = "Karla Salamun and Hrvoje Dzapo",
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title = "A Cooperative Coevolutionary Approach to Designing
Acceptance Tests for Jobs With Weakly Hard Real-Time
Constraints",
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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, Scheduling
algorithms, Heuristic algorithms, Multimedia systems,
Dynamic scheduling, Control systems, Real-time systems,
Computational efficiency, weakly hard real-time
systems, acceptance tests, cooperative coevolution",
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isbn13 = "979-8-3503-0837-2",
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DOI = "doi:10.1109/CEC60901.2024.10612030",
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abstract = "Some real-time applications, including control
sys-tems, communication systems, and multimedia
systems, allow the relaxation of hard real-time
constraints in the sense of tolerating deadline
violations under certain conditions. The conditions
that specify the ratio and distribution of tolerable
deadline violations are defined through the weakly hard
real-time constraints, and real-time systems that
implement these constraints are referred to as weakly
hard real-time systems. The majority of the existing
approaches for scheduling tasks with weakly hard
real-time constraints rely on classifying task
instances, i.e., jobs, as either mandatory or optional
(skippable), and provide a mechanism for ensuring
timely completion of mandatory jobs, while aiming to
maximize the number of timely completed optional jobs.
However, few approaches implement online acceptance
tests for optional jobs which ensure that jobs that are
unable to be completed before their respective deadline
are not accepted into the system. The existing
acceptance tests for optional jobs have high
computational complexity with respect to the number of
tasks and are therefore not suitable for implementation
on real-time computing platforms with limited
resources. In this research, we develop a scheduling
approach that maximizes the number of completed jobs
and implements computationally efficient acceptance
tests that ensure early rejection of jobs that cannot
be completed before their deadline. This is achieved
through a genetic programming approach with cooperative
coevolution. Our approach uses two scheduling
heuristics: a heuristic for dynamic job priority
assignment, and a heuristic for evaluating acceptance
for newly activated jobs. The results show that the
proposed approach achieves comparable performance to
state-of-the-art algorithms in terms of service
quality, but with significantly lower computational
overhead. Unlike the state-of-the-art scheduling
algorithms, the scheduling heuristics generated by the
proposed approach are suitable for implementation in
real-time computing platforms due to their low
computational complexity.",
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notes = "also known as \cite{10612030}
WCCI 2024",
- }
Genetic Programming entries for
Karla Salamun
Hrvoje Dzapo
Citations