Improving genetic algorithm performance by population initialisation with dispatching rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{VLASIC:2019:CIE,
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author = "Ivan Vlasic and Marko Durasevic and
Domagoj Jakobovic",
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title = "Improving genetic algorithm performance by population
initialisation with dispatching rules",
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journal = "Computer \& Industrial Engineering",
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volume = "137",
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pages = "106030",
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year = "2019",
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ISSN = "0360-8352",
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DOI = "doi:10.1016/j.cie.2019.106030",
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URL = "http://www.sciencedirect.com/science/article/pii/S0360835219304899",
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keywords = "genetic algorithms, genetic programming, Scheduling,
Unrelated machines environment, Dispatching rules,
Population initialisation",
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abstract = "Scheduling is an important process that is present in
many real world scenarios where it is essential to
obtain the best possible results. The performance and
execution time of algorithms that are used for solving
scheduling problems are constantly improved. Although
metaheuristic methods by themselves already obtain good
results, many studies focus on improving their
performance. One way of improvement is to generate an
initial population consisting of individuals with
better quality. For that purpose a variety of methods
can be designed. The benefit of scheduling problems is
that dispatching rules (DRs), which are simple
heuristics that provide good solutions for scheduling
problems in a small amount of time, can be used for
that purpose. The goal of this paper is to analyse
whether the performance of genetic algorithms can be
improved by using such simple heuristics for
initialising the starting population of the algorithm.
For that purpose both manual and different kinds of
automatically designed DRs were used to initialise the
starting population of a genetic algorithm. In case of
the manually designed DRs, all existing DRs for the
unrelated machines environment were used, whereas the
automatically designed DRs were generated by using
genetic programming. The obtained results clearly
demonstrate that using populations initialised by DRs
leads to a significantly better performance of the
genetic algorithm, especially when using automatically
designed DRs. Furthermore, it is also evident that such
a population initialisation strategy also improves the
convergence speed of the algorithm, since it allows it
to obtain significantly better results in the same
amount of time. Additionally, the DRs have almost no
influence on the execution speed of the genetic
algorithm since they construct the schedule in time
which is negligible when compared to the execution of
the genetic algorithm. Based on the obtained results it
can be concluded that initialising individuals by using
DRs significantly improves both the convergence and
performance of genetic algorithm, without the need of
having to manually design new complicated
initialisation procedures and without increasing the
execution time of the genetic algorithm",
- }
Genetic Programming entries for
Ivan Vlasic
Marko Durasevic
Domagoj Jakobovic
Citations