Skip to main content

Advertisement

SpringerLink
Log in

Integrated fuzzy-connective-based aggregation network with real-valued genetic algorithm for quality of life evaluation

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Quality of life evaluation is important in national goal setting, program benefit evaluation, and priority ranking of resource allocation. However, the relationship between individual measures and overall evaluation of the quality of life is highly complex. The effectiveness of integrating fuzzy-connective-based aggregation network with real-valued genetic algorithm (GA) in quality of life evaluation is investigated. The fuzzy-connective-based aggregation network aggregates the relative status or achievement among states in quality of life-related variables through a hierarchical decision-making structure. The aggregation network then produces an overall quality of life evaluation from various aspects. Integration with real-valued GA helps avoid stopping at local solutions, as experienced by conventional fuzzy-connective-based aggregation networks. The drawbacks in binary GA are also prevented. The effectiveness and applicability of integrating fuzzy-connective-based aggregation networks with real-valued GA for quality of life evaluation is confirmed through statistical analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Chang CA, Su CT (1995) A comparison of statistical regression and neural network methods in modeling measurement errors for computer vision inspection systems. Comput Ind Eng 28:593–603

    Article  Google Scholar 

  2. Grabisch M, Marichal JL, Mesiar R, Pap E (1992) Aggregation functions. Cambridge University Press, New York

    Google Scholar 

  3. Chiang JH, Chen YC (2002) Incorporating fuzzy operators in the decision network to improve classification reliability. Comput Electr Eng 28:547–560

    Article  MATH  Google Scholar 

  4. Krishnapuram R, Lee J (1992) Fuzzy-connective-based hierarchical aggregation networks for decision making. Fuzzy Set Syst 46:11–27

    Article  MathSciNet  Google Scholar 

  5. Krishnapuram R, Lee J (1992) Fuzzy-set-based hierarchical networks for information fusion in computer vision. Neural Netw 5:335–350

    Article  Google Scholar 

  6. Torra V (2003) Information fusion in data mining. Springer, New York

    MATH  Google Scholar 

  7. Sexton RS, Dorsey RE, Johnson JD (1998) Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation. Decis Support Syst 22:171–185

    Article  Google Scholar 

  8. Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18:135–140

    Article  Google Scholar 

  9. Liang YH (2009) Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan. Neural Comput Appl 18:833–841

    Article  Google Scholar 

  10. Leung KF, Leung FHF, Lam HK, Ling SH (2007) Application of a modified neural fuzzy network and an improved genetic algorithm to speech recognition. Neural Comput Appl 16:419–431

    Article  Google Scholar 

  11. Wu CH, Tzeng GH, Goo YJ, Fang WC (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32:397–408

    Article  Google Scholar 

  12. Pan WT (2009) Forecasting classification of operating performance of enterprises by ZSCORE combining ANFIS and genetic algorithm. Neural Comput Appl 18:1005–1011

    Article  Google Scholar 

  13. Dyckhoff H, Pedrycz W (1984) Generalized means as a model of compensation connectives. Fuzzy Set Syst 14:143–154

    Article  MathSciNet  MATH  Google Scholar 

  14. Zimmermann HJ, Zysno P (1980) Latent connectives in human decision making. Fuzzy Set Syst 4:37–51

    Article  MATH  Google Scholar 

  15. Rumelhart DE, McClelland JM, the PDP Research Group (1986) Parallel distributed processing. The MIT Press, Cambridge

    Google Scholar 

  16. Zimmermann HJ, Zysno P (1983) Decision and evaluations by hierarchical aggregation of information. Fuzzy Set Syst 10:243–260

    Article  MATH  Google Scholar 

  17. Zimmermann HJ (1991) Fuzzy set theory and its applications, 2nd edn. Maw Chang Book Company, Taipei

    MATH  Google Scholar 

  18. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, New York

    MATH  Google Scholar 

  19. Holland JH (1975) Adaption in natural and artificial systems. The University of Michigan Press, Ann Arbor

    MATH  Google Scholar 

  20. Herrera F, Lozano M, Verdegay JL (1998) Tackling real-coded genetic algorithms: operators and tools for behavioral analysis. Artif Intell Rev 12:265–319

    Article  MATH  Google Scholar 

  21. Dorsey RE, Mayer WJ (1995) Genetic algorithms for estimation problems with multiple optima, nondifferentiability, and other irregular features. J Bus Econ Stat 13(1):53–66

    Google Scholar 

  22. Su MC, Chang HT (2000) Application of neural networks incorporated with real-valued genetic algorithms in knowledge acquisition. Fuzzy Set Syst 112:85–97

    Article  Google Scholar 

  23. Haupt RL, Haupt SE (2004) Practical genetic algorithms, 2nd edn. Wiley, New Jersey

    MATH  Google Scholar 

  24. Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, New York

    MATH  Google Scholar 

  25. Wright AH (1991) Genetic algorithms for real parameter optimization. In: Rawlin GJE (ed) Foundations of genetic algorithms. Morgan Kaufmann, San Mateo, pp 205–218

    Google Scholar 

  26. Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer, New York

    MATH  Google Scholar 

  27. De Jong KA (1975) An analysis of the behavior of a class of genetic adaptive systems. Dissertation, University of Michigan

  28. Liu BC (1988) Energy, income, and quality of life management in USA: an information systems approach to decision analysis. Tamkang University, Taipei

    Google Scholar 

  29. Herrera EW, Islam G, Hamilton M (2009) Subjective well-being in cities: a multidimensional concept of individual, social and cultural variables. Appl Res Qual Life 4:201–221

    Article  Google Scholar 

  30. Cracolici MF, Giambona F, Cuffaro M (2011) The determinants of subjective economic well-being: an analysis on Italian–Silc data. Appl Res Qual Life. doi:10.1007/s11482-011-9140-z

    Google Scholar 

  31. Yonk RM, Reilly S (2011) Citizen involvement & quality of life: exit, voice and loyalty in a time of direct democracy. Appl Res Qual Life. doi:10.1007/s11482-011-9142-x

    Google Scholar 

  32. Nzeadibe TC, Ajaero CK (2010) Assessment of socio-economic characteristics and quality of life expectations of rural communities in Enugu State, Nigeria. Appl Res Qual Life 5:353–371

    Article  Google Scholar 

  33. Farooq U, Guo X, Chuang LH, Fang H, Zhuang G, Xia C (2011) Measuring health-related quality of life in Kashin–Beck disease using EQ-5D. Qual Life Res 20:425–429

    Article  Google Scholar 

  34. Liu Z, Liu A, Wang C, Niu Z (2004) Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Future Gener Comput Syst 20:1119–1129

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by grants from National Science Council, Taiwan, R.O.C.

Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Chao-Ton Su & Fang-Fang Wang

Authors
  1. Chao-Ton Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fang-Fang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao-Ton Su.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Su, CT., Wang, FF. Integrated fuzzy-connective-based aggregation network with real-valued genetic algorithm for quality of life evaluation. Neural Comput & Applic 21, 2127–2135 (2012). https://doi.org/10.1007/s00521-011-0644-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-011-0644-0

Keywords

Search

Navigation