Abstract
Currently, the evolutionary computing techniques are increasingly used in different fields, such as optimization, machine learning, and others. The starting point of the investigation is a set of optimization tools based on these techniques and one of them is called evolutionary grammar [1]. It is a evolutionary technique derived from genetic algorithms and used to generate programs automatically in any type of language.
The present work is focused on the design and evaluation of hardware acceleration technique through PSoC, for the execution of evolutionary grammar. For this, a ZYNQ development platform is used, in which the logical part is used to implement factory modules and independents hardware blocks made up of a soft-processor, memory BRAM, and a CORDIC module developed to perform arithmetic operations. The processing part is used for the execution of the algorithm. Throughout the development, the procedures and techniques used for hardware and software design are specified, and the viability of the implementation is analyzed considering the comparison of the algorithm execution times in Java versus the execution times in Hardware.
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References
Nicolau, M., Agapitos, A.: Understanding grammatical evolution: grammar design. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 23–53. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_2
Kramer, O.: Genetic Algorithm Essentials. SCI, vol. 679. (2017). https://doi.org/10.1007/978-3-319-52156-5
De Silva, A.M., Leong, P.H.W.: Grammatical evolution. SpringerBriefs Appl. Sci. Technol. 5, 25–33 (2015). https://doi.org/10.1007/978-981-287-411-5_3
O’Neill, M., Brabazon, A.: Grammatical swarm: the generation of programs by social programming. Nat. Comput. 5, 443–462 (2006). https://doi.org/10.1007/s11047-006-9007-7
Le Goues, C., Yoo, S. (eds.): SSBSE 2014. LNCS, vol. 8636. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-09940-8
Colmena, J.: HEuRistic optimization (2016). GitHub repositor. https://github.com/jlrisco/hero
Xilinx Inc: AXI reference guide UG761 (v13.1). 761 (2011)
Chapman, K.: PicoBlaze for Spartan-6, Virtex-6, 7-Series, Zynq and UltraScale Devices (KCPSM6). 1–24 (2014)
Dma, A.X.I.: Table of contents. Nippon Ronen Igakkai Zasshi. Japanese J. Geriatr. 56, Contents1-Contents1 (2019). https://doi.org/10.3143/geriatrics.56.contents1
Volder, J.: The CORDIC computing technique, pp. 257-261 (2008). https://doi.org/10.1145/1457838.1457886
Ryan, C., O’Neill, M., Collins, J.J.: Introduction to 20 years of grammatical evolution. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 1–21. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_1
Lourenço, N., Assunção, F., Pereira, F.B., Costa, E., Machado, P.: Structured grammatical evolution: a dynamic approach. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 137–161. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_6
Grifoni, P., D’Ulizia, A., Ferri, F.: Computational methods and grammars in language evolution: a survey. Artif. Intell. Rev. 45(3), 369–403 (2015). https://doi.org/10.1007/s10462-015-9449-3
Assuncao, F., Lourenco, N., Machado, P., Ribeiro, B.: Automatic generation of neural networks with structured Grammatical Evolution. In: Proceedings of the 2017 IEEE Congress on Evolutionary Computation CEC 2017, pp. 1557–1564 (2017). https://doi.org/10.1109/CEC.2017.7969488
Borlikova, G., Smith, L., Phillips, M., O’Neill, M.: Business analytics and grammatical evolution for the prediction of patient recruitment in multicentre clinical trials. In: Ryan, C., O’Neill, M., Collins, J.J. (eds.) Handbook of Grammatical Evolution, pp. 461–486. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-78717-6_19
Contreras, I., Bertachi, A., Biagi, L., Oviedo, S., Vehí, J.: Using grammatical evolution to generate short-term blood glucose prediction models. In: CEUR Workshop Proceedings, vol. 2148, pp. 91–96 (2018)
Merelo, J.J., et al.: Benchmarking languages for evolutionary algorithms. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9598, pp. 27–41. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31153-1_3
Craven, S., Athanas, P.: Examining the Vi-ability of FPGA Supercomputing. EURASIP J. Embed. Syst. 93652 (2007). https://doi.org/10.1155/2007/93652
Vega-Rodríguez, M.A., Gutiérrez-Gil, R., Ávila-Román, J.M., Sánchez-Pérez, J.M., Gómez-Pulido, J.A.: Genetic algorithms using parallelism and FPGAs: the TSP as case study. In: Proceedings of the International Conference on Parallel Processing Workshop 2005, pp. 573–579 (2005). https://doi.org/10.1109/ICPPW.2005.36
Hill, M.D., Marty, M.R.: Amdahl’s law in the multicore era. Computer (Long. Beach. Calif) 41, 33–38 (2008). https://doi.org/10.1109/MC.2008.209
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Vallejo Mancero, B., Zapata, M., Topón - Visarrea, L., Malagón, P. (2020). Design and Evaluation of a Heuristic Optimization Tool Based on Evolutionary Grammars Using PSoCs. In: Cicirelli, F., Guerrieri, A., Pizzuti, C., Socievole, A., Spezzano, G., Vinci, A. (eds) Artificial Life and Evolutionary Computation. WIVACE 2019. Communications in Computer and Information Science, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-45016-8_11
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