Abstract
Embedded systems need to execute some tasks. The designer needed to predict every behaviour of the system. However the problem appears when system meets unexpected situation. In some cases the system architecture cannot be modified or such operation is too expensive. In this paper we present a novel developmental genetic programming based method for assignment of unexpected tasks in embedded system design process. Our approach does not modify the system architecture. The proposed method evolves decision trees. The new individuals are obtained during evolution process after using genetic operators: mutation, crossover, cloning and selection. After genotype to phenotype mapping the ready system is obtained.
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References
Guo, C., Ci, S., Yang, Y.: A servey of energy consumption measurement in embedded system. IEEE Access 9, 60516–60530 (2021)
Laohapensaeng, T., Chaisricharoen, R., Boonyanant, P.: Evaluation system for car engine performance. In: Proceedings of Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, Thailand, pp. 322–326 (2021)
Lopez-Montiel, M., Orozco-Rosas, U., Sanchez-Adame, M., Picos, K., Ross, O.H.M.: Evaluation method of deep learning-based embedded systems for traffic sign detection. IEEE Access 9, 101217–101238 (2021)
Wolf, W., Ozer, B., Lv, T.: Smart cameras as embedded systems. Computer 35, 48–53 (2002)
Saddik, A., Latif, R., El Ouardi, A., Elhoseny, M., Khelifi, A.: Computer development based embedded systems in precision agriculture: tools and application. Acta Agric. Scand. Sect. B Soil Plant Sci. 72(1), 589–611 (2022)
De Micheli, G., Gupta, R.: Hardware/software co-design. Proc. IEEE 95(3), 349–365 (1997)
Górski, A., Ogorzałek, M.: Genetic programming based algorithm for HW/SW cosynthesis of distributed embedded systems specified using conditional task graph. In: Proceedings of the 10th International Conference on Sensor Networks, pp. 239–243 (2022)
Górski, A., Ogorzałek, M.: Genetic programming based constructive algorithm with penalty function for hardware/software cosynthesis of embedded systems. In: Proceedings of the 16th International Conference on Software Technologies, ICSOFT, pp. 583–588 (2021)
Oh, H., Ha, S.: Hardware-software cosynthesis of multi-mode multi-task embedded systems with real-time constraints. In: Proceedings of the International Workshop on Hardware/Software Codesign, pp. 133–138 (2002)
Dick, R.P., Jha, N.K.: MOGAC: a multiobjective genetic algorithm for the co-synthesis of hardware-software embedded systems. IEEE Trans. Comput. Aided Des. Integr. Circ. Syst. 17(10), 920–935 (1998)
Deniziak, S., Gorski, A.: Hardware/software co-synthesis of distributed embedded systems using genetic programming. In: Hornby, G.S., Sekanina, L., Haddow, P.C. (eds.) ICES 2008. LNCS, vol. 5216, pp. 83–93. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85857-7_8
Górski, A., Ogorzałek, M.: Genetic programming based iterative improvement algorithm for HW/SW cosynthesis of distributted embedded systems. In: Proceedings of the 10th International Conference on Sensor Networks, pp. 120–125 (2021)
Langdon, W.B.: Genetic programming convergence. In: Genetic Programming and Evolvable Machines (2021)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(2), 95–99 (1988)
Koza, J.R., Bennett III, F.H., Lohn, J., Dunlap, F., Keane, M.A., Andre, D.: Automated synthesis of computational circuits using genetic programming. In: Proceedings of the IEEE Conference on Evolutionary Computation (1997)
Górski, A., Ogorzałek, M.: Adaptive iterative improvement GP-based methodology for HW/SW co-synthesis of embedded systems. In: Proceedings of the 7th International Joint Conference on Pervasive and Embedded Computing and Communication Systems, Spain, pp. 56–59 (2017)
Kefer, K., et al.: Simulation-based optimization of residential energy flows using white box modeling by genetic programming. Energy Build. 258, 111829 (2022)
Andelić, N., Baressi Šegota, S., Lorencin, I., Mrzljak, V., Car, Z.: Estimation of COVID-19 epidemic curves using genetic programming algorithm. Health Inform. J. 27(1) (2021)
Poli, R., Langdon, W., McPhee, N.: A field guide to genetic programming (2008). http://lulu.com. http://www.gp-field-guide.org.uk
Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming - An Introduction. On the Automatic Evolution of Computer Programs and Its Application. dpunkt/Morgan Kaufmann, Heidelberg/San Francisco (1998)
Miller, J.F.: Cartesian genetic programming. In: Miller, J. (ed.) Cartesian Genetic Programming, pp. 17–34. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-17310-3_2
Górski, A., Ogorzałek, M.: Assignment of unexpected tasks for a group of embedded systems. IFAC-PapersOnLine 51(6), 102–106 (2018)
Górski, A., Ogorzałek, M.: Assignment of unexpected tasks in embedded system design process. Microprocess. Microsyst. 44, 17–21 (2016)
Górski, A., Ogorzałek, M.: Adaptive GP-based algorithm for hardware/software co-design of distributed embedded systems. In: Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems, Portugal, pp. 125–130 (2014)
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Górski, A., Ogorzałek, M. (2024). Assignment of Unexpected Tasks in Embedded System Design Process Using Genetic Programming. In: Moosaei, H., Hladík, M., Pardalos, P.M. (eds) Dynamics of Information Systems. DIS 2023. Lecture Notes in Computer Science, vol 14321. Springer, Cham. https://doi.org/10.1007/978-3-031-50320-7_7
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