Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers

Anabel Martínez-Vargas; M. A. Cosío-León; Andrés J. García-Pérez; Oscar Montiel

doi:10.1007/978-3-030-68776-2_3

Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers

Anabel Martínez-Vargas, M. A. Cosío-León, Andrés J. García-Pérez, Oscar Montiel

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

In this chapter, we describe the implementation and evaluation of a distributed steady-state genetic algorithm on low-power computers. Specifically, we integrate the NVIDIA Jetson card and the ESP32 cards to form a master-slave model. NVIDIA Jetson card is the master, whereas the ESP32 cards are the slaves. We evaluated the distributed steady-state genetic algorithm on two challenging combinatorial problems: the n-Queens problem and the travelling salesman problem. To compare the performance of the distributed steady-state genetic algorithm on those well-known problems, we implemented a sequential steady-state genetic algorithm. The simulation results indicate that the distributed steady-state genetic algorithm can escape local minima in the travelling salesman problem; hence, the solutions have a better quality of fitness than the sequential genetic algorithm ones. In contrast, for the n-Queens problem, both genetic algorithms’ performance is remarkably similar.

Original language	English
Title of host publication	Studies in Computational Intelligence
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	41-70
Number of pages	30
DOIs	https://doi.org/10.1007/978-3-030-68776-2_3
State	Published - 2021
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	940
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Keywords

Distributed steady-state genetic algorithm
ESP32
Master-slave model
NVIDIA jetson
Travelling salesman problem
n-Queens problem

Access to Document

10.1007/978-3-030-68776-2_3

Cite this

Martínez-Vargas, A., Cosío-León, M. A., García-Pérez, A. J., & Montiel, O. (2021). Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers. In Studies in Computational Intelligence (pp. 41-70). (Studies in Computational Intelligence; Vol. 940). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-68776-2_3

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abstract = "In this chapter, we describe the implementation and evaluation of a distributed steady-state genetic algorithm on low-power computers. Specifically, we integrate the NVIDIA Jetson card and the ESP32 cards to form a master-slave model. NVIDIA Jetson card is the master, whereas the ESP32 cards are the slaves. We evaluated the distributed steady-state genetic algorithm on two challenging combinatorial problems: the n-Queens problem and the travelling salesman problem. To compare the performance of the distributed steady-state genetic algorithm on those well-known problems, we implemented a sequential steady-state genetic algorithm. The simulation results indicate that the distributed steady-state genetic algorithm can escape local minima in the travelling salesman problem; hence, the solutions have a better quality of fitness than the sequential genetic algorithm ones. In contrast, for the n-Queens problem, both genetic algorithms{\textquoteright} performance is remarkably similar.",

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Martínez-Vargas, A, Cosío-León, MA, García-Pérez, AJ & Montiel, O 2021, Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers. in Studies in Computational Intelligence. Studies in Computational Intelligence, vol. 940, Springer Science and Business Media Deutschland GmbH, pp. 41-70. https://doi.org/10.1007/978-3-030-68776-2_3

Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers. / Martínez-Vargas, Anabel; Cosío-León, M. A.; García-Pérez, Andrés J. et al.
Studies in Computational Intelligence. Springer Science and Business Media Deutschland GmbH, 2021. p. 41-70 (Studies in Computational Intelligence; Vol. 940).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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Performance Analysis of a Distributed Steady-State Genetic Algorithm Using Low-Power Computers

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