Genetic-Fuzzy Data Envelopment Analysis Model for Evaluating Financial Institutions Relative Productivity in a Fluctuating Economic Market

1. Toma E., Carina D., Ion D., and Elena C. DEA applicability in Assessment of Agriculture Efficiency on Areas with Similar Geographically Patterns. Agriculture and Agricultural Science Procedia. 6: 704-711. (2015).

2. Emrouznejad A., and De Witte K. COOPER-framework: A Unified Process for Non-parametric Projects. European Journal of Operational Research. 207: 1573-1586 (2010).

3. Micheal O. Relative Efficiency of Commercial Banks in Nigeria: A Nonparametric mathmatical optimization Analysis. International Journal of Economics and Financial Research, 2: 27-49 (2017).

4.           Bogetoft P., and Otto L. Benchmarking with DEA, SFA, and R. Springer-Verlag, New York, 352p. (2011).

5. Madhanagopal R., and Chandrasekaran R. Selecting Appropriate Variables for DEA Using Genetic Algorithm (GA) Search Procedure. International Journal of Data Envelopment Analysis and Operations Research. 1: 28-33 (2014).

6. Chen Y. C., Huang C., and Tu C. The analysis of bank business performance and market risk-Apply Fuzzy DEA. Ecomomic Modelling. 32: 225-232 (2013).

7. Tahmasebi P., and Hezarkhani A. A hybrid neural network-fuzzy logic-genetic algorithm for grade estimation. Computers & Geosciences, 42: 18-27 (2012).

8. Skale M., and Rabar D. Measuring Economic growth using Deta evelopment Analysis. Amfiteatru Economic, 18 (42), 386-406. (2015).

9. Zhu J. Multi-factor performance measure model with an application to Fortune 500    companies. European Journal of Operational Research, 1: 105-124. (2003).

10. Cooper W., Seiford L. M., and Tone K. Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer, New York (2007).

11. Widiarto I., & Emrouznejad A. Social and financial efficiency of Islamic microfinance   institutions: A Data Envelopment Analysis application. Socio-Economic Planning Sciences, 50 (1): 1-17 (2015).

12. Cook, W. and Zhu J. Modeling Performance Measurement: Applications and Implementation Issues in DEA. Springer, New York, (2005).

13. Livi L., Rizzia A., & Sadeghianba A. Granular modeling and computing approaches for intelligent analysis of non-geometric data. Applied Soft Computing. 27: 567-574 (2015).

14. Pedrycz W. Granular Computing: Analysis and Design of Intelligent Systems (Industrial Electronics Series). Taylor & Francis, New York, (2013).

15. Kau C., and Liu S. Fuzzy efficiency measures in data envelopment analysis. Fuzzy Sets and Systems. 113: 427-437 (2000).

16. Wen M., and Li H. Fuzzy data envelopment analysis (DEA): Model and ranking method. Journal of Computational and Applied Mathematics, 223: 872–878. (2009).

17. Lertworasirikul S., Fang S., Joines J., and Nuttle H. Fuzzy Data Envelopment Analysis (DEA): A possibility approach. Fuzzy Sets and Systems. 139: 379-394 (2003).

18. Hatami-Marbini A., Emrouznejad A., and Tavana M. Taxonomy and review of the Fuzzy Data Envelopment Analysis literature: two decades in the making. European Journal of Operational Research. 214: 457-472 (2011).

19. Houssine T., and Houssine A. Assessing the Efficiency of commercial Tunisian Banks using Fuzzy Data Envelopment Analysis. Journal of Data Envelopment Analysis and Decision Science. 2: 14-27 (2017).

20. Wen M., You C., and Kang, R. A new ranking method to fuzzy data envelopment analysis. Computers and Mathematics with Applications. 59: 3398-3404 (2010).

21. Wanke P., Barros C., and Emrouznejad A. Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping: A case of Mozambican banks. European Journal of Operational Research, 249: 378-389 (2016).

22. Wanke P., Azad A., and Emrouznejad A. Efficiency in BRICS banking under data vagueness: a two-stage fuzzy approach. Global Finance Journal. 35: 58-71 (2017).

23. Kazemi M., and  Alimi A. A fully fuzzy approach to data envelopment analysis. Journal of Mathematics and Computer Sciences. 11: 238-245 (2014).

24. Markovits-Somoogyi R. Ranking efficient and inefficient decision making units in data envelopment analysis. International Journal for Trafffic and Transport Engineering. 1(4): 245-256 (2011).

25. Elena T., Dobre C., Dona I., and Cofas E. DEA applicability in assessment of agriculture efficiency on areas with similar geographical patterns. International Conference on Agricuture for life, Life for Agriculture, Elsevier, Bucharest, pp. 704-711 (2015).

26. Saati M., Memariani A., and Jahanshahloo G. Efficiency Analysis and rnaking of DMUs with fuzzy data. Fuzzy Optimization and Decision Making, 1: 255-267 (2002).

27. Zerafat M., Emrouznejad A., and  Mustafa A. Fuzzy assessment of performance of a decision making units using DEA: A non-radial approach. Expert Systems with Application, 37: 5153-5157 (2010).

28. Sivanandam S. S. Introduction to Neural Network using MATLAB 6.0. McGraw-Hill Education, New York, (2006).

29. Cadima, J., Cerdeira, J. and Minhoto, M. Computational aspects of algorithms for variable selection in the context of principal components. Computational Statistics & Data Analysis. 47: 225 – 236 (2004).

30. Hanen H., Emrouznejad A., and Ouertani M. Technical efficiency detereminants within a Dual Banking System:a DEA bootstrap approach. Aston University, Brimingham, (2013).

31. Kao c., and Liu S. Predicting bank performance with financial forecasts: a case of Taiwan commercial banks. Journal of Banking and Finance. 28: 2353-2368 (2004).

32. Cooper W., Seiford L., and  Zhu J. Handbook on Data Envolopmkment Analysis. Springer, New York,  39 (2011).