This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.


fuzzy cognitive map; predictive model; evolutionary computation; machine learning

Acampora G., Pedrycz W., Vitiello A.: A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps. IEEE Transactions on Fuzzy Systems 23(6)/2015, 2397–2411.

Ahmadi S., Alizadeh S., Forouzideh N., Yeh C., Martin R. L., Papageorgiou E.: ICLA: Imperialist Competitive Learning Algorithm for Fuzzy Cognitive Map. Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 2014.

Arabas J.: Wykłady z algorytmów ewolucyjnych, WNT, Warszawa 2001.

Berry A., Vamplew P.: PoD Can Mutate: A Simple Dynamic Directed Mutation Approach for Genetic Algorithms. Proceedings of AISAT 2004: The 2nd International Conference on Artificial Intelligence in Science and Technology, 2004, 200–205.

Fanaee-T H., Gama J.: Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, Springer Berlin Heidelberg, 2013, 1–15.

Froelich W., Papageorgiou E.: Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series. Papageorgiou E.I.: Fuzzy Cognitive maps for Applied Sciences and Engineering From fundamentals to extensions and learning algorithms. Intelligent Systems Reference Library 54/2014, 121–131.

Goldberg D. E.: Algorytmy genetyczne i ich zastosowania. WNT, Warszawa 1995.

Homenda W., Jastrzebska A., Pedrycz W.: Modeling Time Series with Fuzzy Cognitive Maps. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 2014, 2055–2062.

Jastriebow A., Kubuś Ł., Poczęta K.: Learning fuzzy cognitive maps using Individually Directional Evolutionary Algorithm. In: Jastriebow A., Worwa K.: Applications of information technologies - theory and practice. Institute for Sustainable Technologies – National Research Institute, Radom 2015, 37–48.

Korejo I., Yang S., Li C.: A Directed Mutation Operator for Real Coded Genetic Algorithms. Applications of Evolutionary Computation 6024/2010, 491–500.

Kosko B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24(1)/1986, 65–75.

Kubuś Ł.: Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems - Comparative Study, International Journal of Intelligent Systems and Applications (IJISA) 7(9)/2015, 12–19.

Michalewicz Z.: Algorytmy genetyczne + struktury danych = programy ewolucyjne. WNT, Warszawa 1999.

Papageorgiou E. I.: Learning Algorithms for Fuzzy Cognitive Maps - A Review Study. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 42(2)/2012, 150–163.

Poczęta K., Yastrebov A.: Analysis of Fuzzy Cognitive Maps with Multi-Step Learning Algorithms in Valuation of Owner-Occupied Homes. 2014 IEEE International Conference on Fuzzy Systems (FUZZIEEE), Beijing, China 2014, 1029–1035.

Poczęta K., A. YastrebovA., Papageorgiou E. I.: Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm. 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland, 2015, 547–554.

Song H., Miao C., Roel W., Shen Z.: Implementation of fuzzy cognitive maps based on fuzzy neural network and application in prediction of time series. IEEE Transactions on Fuzzy Systems 18(2)/2010, 233–250.

Stach W., Kurgan L., Pedrycz W., Reformat M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems 153(3)/2005, 371–401.

Stach W., Pedrycz W., Kurgan L. A.: Learning of fuzzy cognitive maps using density estimate. IEEE Trans. on Systems, Man, and Cybernetics, Part B, vol. 42(3)/2012, 900–912.

Tang P., Tseng M.: Adaptive directed mutation for real-coded genetic algorithms. Applied Soft Computing 13(1)/2013, 600–614.

Yesil E., Urbas L.: Big bang: big crunch learning method for fuzzy cognitive maps. World Acad. Sci. Eng. Technol. 71/2010, 815–8124.

Published : 2017-12-21

Jastriebow, A., Kubuś, Łukasz, & Poczęta, K. (2017). APPLICATION OF FUZZY COGNITIVE MAP TO PREDICT OF EFFECTIVENESS OF BIKE SHARING SYSTEMS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(4), 70-73.

Aleksander Jastriebow
Politechnika Świętokrzyska, Katedra Systemów Informatycznych  Poland
Łukasz Kubuś 
Politechnika Świętokrzyska, Katedra Systemów Informatycznych  Poland
Katarzyna Poczęta 
Politechnika Świętokrzyska, Katedra Systemów Informatycznych  Poland