Abstract
During the last decades, energy consumption has become a topic of interest for algorithm designers, particularly when devoted to networked devices and mainly when handheld ones are involved. Moreover energy consumption has become a matter of paramount importance in nowadays environmentally conscious society. Although a number of studies are already available, not many have focused on Evolutionary Algorithms (EAs). Moreover, no previous attempt has been performed for modeling energy consumption behavior of EAs considering different hardware platforms. This paper thus aims at not only analyzing the influence of the main EA parameters in their energy related behavior, but also tries for the first time to develop a model that allows researchers to know how the algorithm will behave in a number of hardware devices. We focus on a specific member of the EA family, namely Genetic Programming (GP), and consider several devices when employed as the underlying hardware platform. We apply a Fuzzy Rules Based System to build the model that allows then to predict energy required to find a solution, given a previously chosen hardware device and a set of parameters for the algorithm.
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References
de Vega, F.F., Pérez, J.I.H., Lanchares, J.: Parallel Architectures and Bioinspired Algorithms, vol. 122. Springer, Heidelberg (2012)
Cotta, C., Fernández-Leiva, A., de Vega, F.F., Chávez, F., Merelo, J., Castillo, P., Bello, G., Camacho, D.: Ephemeral computing and bioinspired optimization - challenges and opportunities. In: 7th International Joint Conference on Evolutionary Computation Theory and Applications, Lisboa, Portugal, pp. 319–324. Scitepress (2015)
Albers, S.: Algorithms for dynamic speed scaling. In: Schwentick, T., Dürr, C. (eds.) 28th International Symposium on Theoretical Aspects of Computer Science (STACS 2011). Leibniz International Proceedings in Informatics (LIPIcs), vol. 9, pp. 1–11. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl (2011)
Kumar, G., Shannigrahi, S.: New online algorithm for dynamic speed scaling with sleep state. Theor. Comput. Sci. 593, 79–87 (2015)
Huang, P., Kumar, P., Giannopoulou, G., Thiele, L.: Energy efficient DVFS scheduling for mixed-criticality systems. In: 2014 International Conference on Embedded Software (EMSOFT), pp. 1–10, October 2014
Chen, Z., Mi, C.C., Xiong, R., Xu, J., You, C.: Energy management of a power-split plug-in hybrid electric vehicle based on genetic algorithm and quadratic programming. J. Power Sources 248, 416–426 (2014)
Yu, W., Li, B., Jia, H., Zhang, M., Wang, D.: Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy Build. 88, 135–143 (2015)
Álvarez, J.D., Risco-Martín, J.L., Colmenar, J.M.: Multi-objective optimization of energy consumption and execution time in a single level cache memory for embedded systems. J. Syst. Softw. 111, 200–212 (2016)
de Vega, F.F., Chávez, F., Díaz, J., García, J.A., Castillo, P.A., Merelo, J.J., Cotta, C.: A cross-platform assessment of energy consumption in evolutionary algorithms. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 548–557. Springer, Cham (2016). doi:10.1007/978-3-319-45823-6_51
Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Gacto, M., Galende, M., Alcalá, R., Herrera, F.: METSK-HDe: a multiobjective evolutionary algorithm to learn accurate tsk-fuzzy systems in high-dimensional and large-scale regression problems. Inf. Sci. 276, 63–79 (2014)
Sugeno, M., Kang, G.: Structure identification of fuzzy model. Fuzzy Sets Syst. 28(1), 15–33 (1988)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985)
Nesmachnow, S., Luna, F., Alba, E.: An empirical time analysis of evolutionary algorithms as C programs. Softw. Pract. Exp. 45(1), 111–142 (2015)
Mamdani, E.H.: Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Trans. Comput. C–26(12), 1182–1191 (1977)
Mamdani, E., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7(1), 1–13 (1975)
Herrera, F.: Genetic fuzzy systems: taxonomy, current research trends and prospects. Evol. Intel. 1(1), 27–46 (2008)
García-Valdez, M., Trujillo, L., Merelo, J.J., de Vega, F.F., Olague, G.: The evospace model for pool-based evolutionary algorithms. J. Grid Comput. 13(3), 329–349 (2015)
Balasubramaniam, J.: Conditions for inference invariant rule reduction in frbs by combining rules with identical consequents. Acta Polytech. Hung. 3(4), 113–143 (2006)
Acknowledgements
We acknowledge support from Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (FEDER) under project EphemeCH (TIN2014-56494-C4-{1,2,3}-P), and Junta de Extremadura FEDER, project GR15068.
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Díaz Álvarez, J., Chávez de La O, F., García Martínez, J.Á., Castillo Valdivieso, P.Á., de Vega, F.F. (2017). Estimating Energy Consumption in Evolutionary Algorithms by Means of FRBS. In: Oliveira, E., Gama, J., Vale, Z., Lopes Cardoso, H. (eds) Progress in Artificial Intelligence. EPIA 2017. Lecture Notes in Computer Science(), vol 10423. Springer, Cham. https://doi.org/10.1007/978-3-319-65340-2_19
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