Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

Summary This work proposes a strategy to create an embedded genetic algorithms (GAs) for low‐power, low‐cost, and low‐size‐memory devices. This strategy aims to provide the means of GAs to run as a low‐cost and low‐power consumption embedded system, where microcontrollers (μCs) are commonly used. Th...

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Main Authors:	Denis R. da S. Medeiros, Matheus F. Torquato, Marcelo A. C. Fernandes
Format:	Article
Language:	English
Published:	Wiley 2020-09-01
Series:	Engineering Reports
Subjects:	embedded system genetic algorithms low‐cost low‐power low‐size‐memory microcontrollers
Online Access:	https://doi.org/10.1002/eng2.12231

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spelling	doaj-eb9f56fab9234acb95a50fd5a75f5e212020-11-25T03:30:22ZengWileyEngineering Reports2577-81962020-09-0129n/an/a10.1002/eng2.12231Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devicesDenis R. da S. Medeiros0Matheus F. Torquato1Marcelo A. C. Fernandes2Laboratory of Machine Learning and Intelligent Instrumentation nPITI‐IMD, Federal University of Rio Grande do Norte Natal BrazilCollege of Engineering Swansea University Swansea UKLaboratory of Machine Learning and Intelligent Instrumentation nPITI‐IMD, Federal University of Rio Grande do Norte Natal BrazilSummary This work proposes a strategy to create an embedded genetic algorithms (GAs) for low‐power, low‐cost, and low‐size‐memory devices. This strategy aims to provide the means of GAs to run as a low‐cost and low‐power consumption embedded system, where microcontrollers (μCs) are commonly used. The implementation details are presented, emphasizing the limitations and restrictions imposed to turn it more compact and efficient. In addition, data related to the algorithm effectiveness, processing time, and memory consumption were obtained from simulations, oscilloscope measurements, and using the hardware‐in‐loop technique. Finally, this implementation is compared with other implementation from the literature and the results show that 8‐bits μCs can run GAs for several practical applications.https://doi.org/10.1002/eng2.12231embedded systemgenetic algorithmslow‐costlow‐powerlow‐size‐memorymicrocontrollers
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Denis R. da S. Medeiros Matheus F. Torquato Marcelo A. C. Fernandes
spellingShingle	Denis R. da S. Medeiros Matheus F. Torquato Marcelo A. C. Fernandes Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices Engineering Reports embedded system genetic algorithms low‐cost low‐power low‐size‐memory microcontrollers
author_facet	Denis R. da S. Medeiros Matheus F. Torquato Marcelo A. C. Fernandes
author_sort	Denis R. da S. Medeiros
title	Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices
title_short	Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices
title_full	Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices
title_fullStr	Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices
title_full_unstemmed	Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices
title_sort	embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices
publisher	Wiley
series	Engineering Reports
issn	2577-8196
publishDate	2020-09-01
description	Summary This work proposes a strategy to create an embedded genetic algorithms (GAs) for low‐power, low‐cost, and low‐size‐memory devices. This strategy aims to provide the means of GAs to run as a low‐cost and low‐power consumption embedded system, where microcontrollers (μCs) are commonly used. The implementation details are presented, emphasizing the limitations and restrictions imposed to turn it more compact and efficient. In addition, data related to the algorithm effectiveness, processing time, and memory consumption were obtained from simulations, oscilloscope measurements, and using the hardware‐in‐loop technique. Finally, this implementation is compared with other implementation from the literature and the results show that 8‐bits μCs can run GAs for several practical applications.
topic	embedded system genetic algorithms low‐cost low‐power low‐size‐memory microcontrollers
url	https://doi.org/10.1002/eng2.12231
work_keys_str_mv	AT denisrdasmedeiros embeddedgeneticalgorithmforlowpowerlowcostandlowsizememorydevices AT matheusftorquato embeddedgeneticalgorithmforlowpowerlowcostandlowsizememorydevices AT marceloacfernandes embeddedgeneticalgorithmforlowpowerlowcostandlowsizememorydevices
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Embedded genetic algorithm for low‐power, low‐cost, and low‐size‐memory devices

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