Abstract
Fuzzy cognitive map (FCM) is a universal tool for modeling dynamic decision support systems. It can be constructed by the experts or learned based on historical data. FCM models learned from data are denser than those created by humans. We developed an evolutionary learning approach for fuzzy cognitive maps based on density and system performance indicators. It allows to select only the most significant connections between concepts and receive the structure more similar to the FCMs initialized by experts. This paper is devoted to the application of the developed approach to model decision support systems with the use of real-life and historical data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Acampora, G., Pedrycz, W., Vitiello, A.: A competent memetic algorithm for learning fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 23(6), 2397–2411 (2015)
Aguilar, J.: A survey about fuzzy cognitive maps papers. Int. J. Comput. Cogn. 3(2), 27–33 (2005)
Andreou, A.S., Mateou, N.H., Zombanakis, G.A.: Soft computing for crisis management and political decision making: the use of genetically evolved fuzzy cognitive maps. Soft Comput. J. 9(3), 194–210 (2005)
Borisov, V.V., Kruglov, V.V., Fedulov, A.C.: Fuzzy Models and Networks. Publishing house Telekom, Moscow (2004). (in Russian)
Buruzs, A., Hatwagner, M.F., Pozna, R.C., Koczy, L.T.: Advanced learning of fuzzy cognitive maps of waste management by bacterial algorithm. In: IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 890–895 (2013)
Chi, Y., Liu, J.: Learning of fuzzy cognitive maps with varying densities using multi-objective evolutionary algorithms. IEEE Trans. Fuzzy Syst. 24, 71-81 (2015)
De Vito, S., Massera, E., Piga, M., Martinotto, L., Di Francia, G.: On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens. Actuators B: Chem. 129(2), 750–757 (2008)
Jastriebow, A., Poczeta, K.: Analysis of multi-step algorithms for cognitive maps learning. Bull. Pol. Acad. Sci. Tech. Sci. 62(4), 735–741 (2014)
Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24(1), 65–75 (1986)
Kubuś, Ł.: Individually directional evolutionary algorithm for solving global optimization problems - comparative study. Int. J. Intell. Syst. Appl. (IJISA) 7(9), 12–19 (2015)
Kubuś, Ł., Poczeta, K., Yastrebov, A.: A new learning approach for fuzzy cognitive maps based on system performance indicators. In: 2016 IEEE International Conference on Fuzzy Systems, Vancouver, Canada, 1398–1404 (2016)
Mateou, N.H., Andreou, A.S.: A framework for developing intelligent decision support systems using evolutionary fuzzy cognitive maps. J. Intell. Fuzzy Syst. 19(2), 150–171 (2008)
Michalewicz, Z.: Genetic Algorithms \(+\) Data Structures \(=\) Evolution Programs. Springer, New York (1996)
Miettinem, K.M.: Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston (1999)
Poczeta, K., Yastrebov, A., Papageorgiou, E.I.: Learning fuzzy cognitive maps using structure optimization genetic algorithm. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Annals of Computer Science and Information Systems. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, vol. 5, pp. 547–554 (2015)
Poczeta, K., Kubuś, Ł., Yastrebov, A., Papageorgiou, E.I.: An economic decision support system based on fuzzy cognitive maps with evolutionary learning algorithm, position papers of the 2016 federated conference on computer science and information systems. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) ACSIS, vol. 9, pp. 95–101 (2016)
Rodriguez-Repiso, L., Setchi, R., Salmeron, J.L.: Modelling IT projects success with fuzzy cognitive maps. Expert Syst. Appl. 32, 543–559 (2007)
Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. Appl. Soft Comput. 12, 3704–3710 (2012)
Silov, V.B.: Strategic Decision-Making in a Fuzzy Environment. INPRO-RES, Moscow (1995). (in Russian)
Słoń, G.: Application of models of relational fuzzy cognitive maps for prediction of work of complex systems. In: Lecture Notes in Artificial Intelligence LNAI, vol. 8467, pp. 307–318. Springer (2014)
Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153(3), 371–401 (2005)
Stach, W., Pedrycz, W., Kurgan, L.A.: Learning of fuzzy cognitive maps using density estimate. IEEE Trans. Syst. Man Cybern. Part B 42(3), 900–912 (2012)
Yesil, E., Urbas, L.: Big bang: big crunch learning method for fuzzy cognitive maps. World Acad. Sci. Eng. Technol. 71, 815–8124 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Poczeta, K., Kubuś, Ł., Yastrebov, A., Papageorgiou, E.I. (2018). Application of Fuzzy Cognitive Maps with Evolutionary Learning Algorithm to Model Decision Support Systems Based on Real-Life and Historical Data. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-59861-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-59861-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59860-4
Online ISBN: 978-3-319-59861-1
eBook Packages: EngineeringEngineering (R0)