A general approach to solving hardware and software partitioning problem based on evolutionary algorithms
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Title
A general approach to solving hardware and software partitioning problem based on evolutionary algorithms
Authors
Keywords
Hardware and software partitioning, Greedy repair and optimization, Genetic algorithm, Particle swarm optimization, Differential evolution, Group theory-based optimization algorithm
Journal
ADVANCES IN ENGINEERING SOFTWARE
Volume -, Issue -, Pages 102998
Publisher
Elsevier BV
Online
2021-04-24
DOI
10.1016/j.advengsoft.2021.102998
References
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- A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
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