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.
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References
Bell, J., and B. Stevens. 2009. A survey of known results and research areas for n-queens. Discrete Mathematics 309: 1–31. https://doi.org/10.1016/j.disc.2007.12.043.
Bozejko, W., and M. Makuchowski. 2009. A fast hybrid tabu search algorithm for the no-wait job shop problem. Computer and Industrial Engineering 56: 1502–1509.
Cantu-Paz, E. 2009. Efficient and accurate parallel genetic algorithms. Springer Science & Business Media.
Corus, D., and P.S. Oliveto. 2019. On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference, 1452–1460. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3321707.3321783.
Del-Ser, J., E. Osaba, D. Molina, X.-S. Yang, S. Salcedo-Sanz, D. Camacho, S. Das, P.N. Suganthan, C.A.C. and F. Herrera. 2019. Bio-inspired computation: Where we stand and what’s next. Swarm Evolutionary Computation 48: 220–250. https://doi.org/10.1016/j.swevo.2019.04.008.
Derrac, J., S. García, D. Molina, and F. Herrera. 2011. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1: 3–18. https://doi.org/10.1016/j.swevo.2011.02.002.
Dyer, J.D., R.J. Hartfield, G.V. Dozier, and J.E. Burkhalter. 2012. Aerospace design optimization using a steady state real-coded genetic algorithm. Applied Mathematics and Computation 218: 4710–4730. https://doi.org/10.1016/j.amc.2011.07.038.
Eiben, A.E., and J.E. Smith. 2015. Introduction to evolutionary computing. Incorporated: Springer Publishing Company.
Fogel, D.B. 2000. What is evolutionary computation? IEEE Spectrum 37: 26–32.
Gong, Y.-J., W.-N. Chen, Z.-H. Zhan, J. Zhang, Y. Li, Q. Zhang, and J.-J. Li. 2015. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Applied Soft Computing 34: 286–300. https://doi.org/10.1016/j.asoc.2015.04.061.
Haupt, R.L., and S.E Haupt. 2004. Advanced applications. In Practical Genetic Algorithms, 151–186. John Wiley & Sons, Ltd. https://doi.org/10.1002/0471671746.ch6.
Jianli, C., C. Zhikui, W. Yuxin, and G. He. 2020. Parallel genetic algorithm for N-Queens problem based on message passing interface-compute unified device architecture. Computational Intelligence. https://doi.org/10.1111/coin.12300.
Lozano, M., F. Herrera, J.R. Cano. 2008. Replacement strategies to preserve useful diversity in steady-state genetic algorithms. Information Sciences (Ny. 178: 4421–4433. https://doi.org/10.1016/j.ins.2008.07.031.
Martínez-Vargas, A., G.L. Rodríguez-Cortés, and O. Montiel-Ross. 2019. Comparative representations of a genetic algorithm to locate unmanned aerial vehicles in disaster zones. Engineering Letters 27: 374–384.
Moreno, J.J., G. Ortega, E. Filatovas, J.A. Martínez, and E.M. Garzón. 2017. Using low-power platforms for evolutionary multi-objective optimization algorithms. Journal Supercomputer 302–315.
Oliveto, P.S., D. Sudholt, and C. Witt. 2020. A tight lower bound on the expected runtime of standard steady state genetic algorithms. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 1323–1331. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3377930.3390212.
Osaba, E., F. Diaz, R. Carballedo, E. Onieva, and P. Lopez. 2014. A study on the impact of heuristic initialization functions in a genetic algorithm solving the n-queens problem. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 1473–1474. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/2598394.2602269.
Sepúlveda, C., O. Montiel, J.M. Cornejo, and R. Sepúlveda. 2018. Estimation of Population Pharmacokinetic Model Parameters Using a Genetic Algorithm. In Fuzzy logic in intelligent system design, ed. P. Melin, O. Castillo, J. Kacprzyk, M. Reformat, and W. Melek, 214–221. Cham: Springer International Publishing.
Slowik, A., and H. Kwasnicka. 2020. Evolutionary algorithms and their applications to engineering problems. Neural Computer Applied 1–17.
Talbi, E.-G. 2009. Common concepts for metaheuristics. In Metaheuristics, 1–86. John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470496916.ch1.
Valenzuela, V.M., C.A. Brizuela, M.A. Cosío-León, and A.D. Romero-Ocaño. 2019. A combination of two simple decoding strategies for the no-wait job shop scheduling problem. In Proceedings of the Genetic and Evolutionary Computation Conference, 864–871. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3321707.3321870.
Acknowledgements
Anabel Martínez-Vargas, M.A. Cosío-León, and Andrés J. García-Pérez thank CITEDI-IPN for sharing the know-how and the NVIDIA Jetson card provided through the SIP project 2020053. Anabel Martínez-Vargas thanks PRODEP for providing ESP32 cards by the grant number 149557.
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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: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_3
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