Evolutionary Algorithms and Their Applications in Intelligent Systems

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Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI 2023)

Abstract

The paper presents applications of evolutionary algorithms in intelligent systems. The research and literature review were performed based on the following scientific databases as Google Scholar, Springer, IEEE Xplore, Science Direct, Web of Science. The popularity of particular evolutionary methods in the aspect of intelligent systems was shown. The main application areas of these methods are also indicated and a brief review of the related literature is provided.

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References

  1. Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Ann Arbor (1975)

    Google Scholar 

  2. Koza, J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  3. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  4. Rechenberg, I.: Evolutionstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Holzboog Verlag (1973)

    Google Scholar 

  5. Fogel, L.J., Owens, A.J., Walsh, M.J.: Artificial Intelligence Thorough Simulated Evolution. Wiley, Ann Arbor (1966)

    MATH  Google Scholar 

  6. Słowik, A., Białko, M.: Modified version of roulette selection for evolution algorithms – the fan selection. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 474–479. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_70

    Chapter  Google Scholar 

  7. Słowik, A.: Steering of balance between exploration and exploitation properties of evolutionary algorithms - mix selection. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS (LNAI), vol. 6114, pp. 213–220. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13232-2_26

    Chapter  Google Scholar 

  8. Slowik, A., Slowik, J.: Multi-objective optimization of surface grinding process with the use of evolutionary algorithm with remembered pareto set. Int. J. Adv. Manuf. Technol. 37(7–8), 657–669 (2008). https://doi.org/10.1007/s00170-007-1013-0

    Article  Google Scholar 

  9. Slowik, A.: Application of evolutionary algorithm to design of minimal phase digital filters with non-standard amplitude characteristics and finite bits word length. Bull. Pol. Acad. Sci. Tech. Sci. 59(2), 125–135 (2011). https://doi.org/10.2478/v10175-011-0016-z

    Article  MATH  Google Scholar 

  10. Slowik, A., Bialko, M.: Design of IIR digital filters with non-standard characteristics using differential evolution algorithm. Bull. Pol. Acad. Sci. Tech. Sci. 55(4), 359–363 (2007)

    Google Scholar 

  11. Słowik, A., Białko, M.: Design and optimization of combinational digital circuits using modified evolutionary algorithm. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 468–473. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24844-6_69

    Chapter  MATH  Google Scholar 

  12. Słowik, A., Białko, M.: Partitioning of VLSI circuits on subcircuits with minimal number of connections using evolutionary algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 470–478. Springer, Heidelberg (2006). https://doi.org/10.1007/11785231_50

    Chapter  Google Scholar 

  13. Szczypta, J., Przybył, A., Cpałka, K.: Some aspects of evolutionary designing optimal controllers. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS (LNAI), vol. 7895, pp. 91–100. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38610-7_9

    Chapter  Google Scholar 

  14. Lapa, K., Cpalka, K., Przybyl, A.: Genetic programming algorithm for designing of control systems. Inf. Technol. Control 47(5), 668–683 (2018)

    Google Scholar 

  15. Lapa, K., Cpalka, K.: Flexible fuzzy PID controller (FFPIDC) and a nature-inspired method for its construction. IEEE Trans. Ind. Inform. 14(3), 1078–1088 (2018). https://doi.org/10.1109/TII.2017.2771953

    Article  Google Scholar 

  16. Shareh, M.B., Bargh, S.H., Hosseinabadi, A.A.R., Slowik, A.: An improved bat optimization algorithm to solve the tasks scheduling problem in open shop. Neural Comput. Appl. 33, 1559–1573 (2021). https://doi.org/10.1007/s00521-020-05055-7

    Article  Google Scholar 

  17. Hosseinabadi, A.A.R., Slowik, A., Sadeghilalimi, M., Farokhzad, M., Shareh, M.B., Sangaiah, A.K.: An ameliorative hybrid algorithm for solving the capacitated vehicle routing problem. IEEE Access 7, 175456–175465 (2019). https://doi.org/10.1109/ACCESS.2019.2957722

    Article  Google Scholar 

  18. Slowik, A., Cpalka, K., Lapa, K.: Multi-population nature-inspired algorithm (MNIA) for the designing of interpretable fuzzy systems. IEEE Trans. Fuzzy Syst. 28(6), 1125–1139 (2020)

    Article  Google Scholar 

  19. Gabryel, M., Cpalka, K., L. Rutkowski: Evolutionary strategies for learning of neuro-fuzzy systems. In: Proceedings of the I Workshop on Genetic Fuzzy Systems, pp. 119–123 (2005)

    Google Scholar 

  20. Zalasiński, M., Cpałka, K., Hayashi, Y.: New fast algorithm for the dynamic signature verification using global features values. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS (LNAI), vol. 9120, pp. 175–188. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19369-4_17

    Chapter  Google Scholar 

  21. Zalasiński, M., Cpałka, K., Hayashi, Y.: A new approach to the dynamic signature verification aimed at minimizing the number of global features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS (LNAI), vol. 9693, pp. 218–231. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-39384-1_20

    Chapter  Google Scholar 

  22. Zalasinski, M., Laskowski, L., Niksa-Rynkiewicz, T., Cpalka, K., Byrski, A., Przybyszewski, K., Trippner, P., Dong, S.: Evolutionary algorithm for selecting dynamic signatures partitioning approach. J. Artif. Intell. Soft Comput. Res. 12(4), 267–279 (2022)

    Article  Google Scholar 

  23. Carreres-Prieto, D., Ybarra-Moreno, J., Garcia, J.T., Cerdam-Cartagena, J.F.: A comparative analysis of neural networks and genetic algorithms to characterize wastewater from led spectrophotometry. J. Environ. Chem. Eng. 11(3), Article ID: 110219 (2023)

    Google Scholar 

  24. Sun, J., Liu, Q., Wang, Y., Wang, L., Song, X., Zhao, X.: Five-year prognosis model of esophageal cancer based on genetic algorithm improved deep neural network. IRBM 44(3), Article ID: 100748 (2023)

    Google Scholar 

  25. Fan, Z., Zi-xuan, X., Ming-hu, W.: State of health estimation for Li-ion battery using characteristic voltage intervals and genetic algorithm optimized back propagation neural network. J. Energy Storage, 57, Article ID: 106277 (2023)

    Google Scholar 

  26. Jayashree, J., Kumar, S.A.: Evolutionary correlated gravitational search algorithm (ECGS) with genetic optimized Hopfield neural network (GHNN) - A hybrid expert system for diagnosis of diabetes. Measurement 145, 551–558 (2019)

    Article  Google Scholar 

  27. Ghanbari, A., Kazemi, S.M.R., Mehmanpazir, F., Nakhostin, M.M.: A Cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowl.-Based Syst. 39, 194–206 (2013)

    Article  Google Scholar 

  28. Kumar, P.G., Victoire, A.A., Renukadevi, P., Devaraj, D.: Design of fuzzy expert system for microarray data classification using a novel genetic swarm algorithm. Expert Syst. Appl. 39(2), 1811–1821 (2012)

    Article  Google Scholar 

  29. Kumari, M., Kanti De, P., Narang, P., Shah, N.H.: Integrated optimization of inventory, replenishment, and vehicle routing for a sustainable supply chain utilizing a novel hybrid algorithm with carbon emission regulation. Expert Syst. Appl. 220, Article ID: 119667 (2023)

    Google Scholar 

  30. Saif-Eddine, A.S., El-Beheiry, M.M., El-Kharbotly, A.K.: An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Eng. J. 10(1), 63–76 (2019)

    Article  Google Scholar 

  31. Wang, C., Liu, Y., Yang, G.: Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy. Socio-Econ. Plan. Sci. 87, part A, Article ID: 101563 (2023)

    Google Scholar 

  32. Terfloth, L., Gasteiger, J.: Neural networks and genetic algorithms in drug design. Drug Discovery Today 6(supplement 2), 102–108 (2001)

    Article  Google Scholar 

  33. Devi, R.V., Sathya, S.S., Coumar, M.S.: Evolutionary algorithms for de novo drug design - a survey. Appl. Soft Comput. 27, 543–552 (2015)

    Article  Google Scholar 

  34. Luukkonen, S., van den Maagdenberg, H.W., Emmerich, M.T.M., van Westen, G.J.P.: Artificial intelligence in multi-objective drug design. Current Opinion Struct. Biol. 79, Article ID: 102537 (2023)

    Google Scholar 

  35. Ha, M.-H., Vu, Q.-A., Truong, V.-H.: Optimum design of stay cables of steel cable-stayed bridges using nonlinear inelastic analysis and genetic algorithm. Structures 16, 238–302 (2018)

    Article  Google Scholar 

  36. Cheng, J.: Optimum design of steel truss arch bridges using a hybrid genetic algorithm. J. Constr. Steel Res. 66(8–9), 1011–1017 (2010)

    Article  Google Scholar 

  37. Srinivas, V., Ramanjaneyulu, K.: An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks. Adv. Eng. Softw. 38(7), 475–487 (2007)

    Article  Google Scholar 

  38. Xue, X., Chen, J.: Matching biomedical ontologies through Compact Differential Evolution algorithm with compact adaption schemes on control parameters. Neurocomputing 458, 526–534 (2021)

    Article  Google Scholar 

  39. Zhang, Y., Lin, M., Yang, Y., Ding, C.: A hybrid ensemble and evolutionary algorithm for imbalanced classification and its application on bioinformatics. Comput. Biol. Chem. 98, Article ID: 107646 (2022)

    Google Scholar 

  40. Reis, D.R., Santos, B.C., Bleicher, L., Zarate, L.E., Nobre, C.N.: Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm. Comput. Biol. Med. 158, Article ID: 106799 (2023)

    Google Scholar 

  41. Wang, Z., Zhang, X., Zhang, Z.K., Sheng, D.: Credit portfolio optimization: a multi-objective genetic algorithm approach. Borsa Istanbul Rev. 22(1), 69–76 (2022)

    Article  Google Scholar 

  42. Liu, Y., Zhou, Y., Niu, J.: Portfolio optimization: a multi-period model with dynamic risk preference and minimum lots of transaction. Finan. Res. Lett. 55, part B, Article ID: 103964 (2023)

    Google Scholar 

  43. Drenovak, M., Rankovic, V., Urosevic, B., Jelic, R.: Mean-Maximum drawdown optimization of Buy-and-Hold portfolios using a multi-objective evolutionary algorithm. Finan. Res. Lett. 46, part A, Article ID: 102328 (2022)

    Google Scholar 

  44. Slowik, A., Cpalka, K.: Hybrid approaches to nature-inspired population-based intelligent optimization for industrial applications. IEEE Trans. Ind. Inform. 18(1), 546–558 (2022)

    Article  Google Scholar 

  45. Cpalka, K., Slowik, A., Lapa, K.: A population-based algorithm with the selection of evaluation precision and size of the population. Appl. Soft Comput. 115, Article ID: 108154 (2022)

    Google Scholar 

  46. Faris, H., et al.: An intelligent system for spam detection and identification of the most relevant features based on evolutionary Random Weight Networks. Inf. Fus. 48, 67–83 (2019)

    Article  Google Scholar 

  47. Mistry, K., Zhang, L., Neoh, S.C., Lim, C.P., Fielding, B.: A micro-GA embedded PSO feature selection approach to intelligent facial emotion recognition. IEEE Trans. Cybernet. 47(6), 1496–1509 (2017)

    Article  Google Scholar 

  48. Hong, H.Y., et al.: Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci. Total Environ. 621, 1124–1141 (2018)

    Article  Google Scholar 

  49. Nemati, M., Braun, M., Tenbohlen, S.: Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Appl. Energy 210, 944–963 (2018)

    Article  Google Scholar 

  50. Asim, M., Abd El-Latif, A.A.: Intelligent computational methods for multi-unmanned aerial vehicle-enabled autonomous mobile edge computing systems. ISA Trans. 132, 5–15 (2023)

    Article  Google Scholar 

  51. Yang, X., Li, X., Gao, Z.Y., Wang, H.W., Tang, T.: A cooperative scheduling model for timetable optimization in subway systems. IEEE Trans. Intell. Transp. Syst. 14(1), 438–447 (2013)

    Article  Google Scholar 

  52. Liu, W.L., Gong, Y.J., Chen, W.N., Liu, Z.Q., Wang, H., Zhang, J.: Coordinated charging scheduling of electric vehicles: a mixed-variable differential evolution approach. IEEE Trans. Intell. Transp. Syst. 21(12), 5094–5109 (2020)

    Article  Google Scholar 

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Slowik, A., Cpalka, K., Hassanien, A.E. (2023). Evolutionary Algorithms and Their Applications in Intelligent Systems. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_13

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