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


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Article Info

DATE

2012-11

AUTHORS

Chao-Ton Su, Fang-Fang Wang

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. More... »

PAGES

2127-2135

References to SciGraph publications

  • 2012-03. The Determinants of Subjective Economic Well-being: An Analysis on Italian-Silc Data in APPLIED RESEARCH IN QUALITY OF LIFE
  • 2009-11. Forecasting classification of operating performance of enterprises by ZSCORE combining ANFIS and genetic algorithm in NEURAL COMPUTING AND APPLICATIONS
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  • 2010-12. Assessment of Socio-Economic Characteristics and Quality of Life Expectations of Rural Communities in Enugu State, Nigeria in APPLIED RESEARCH IN QUALITY OF LIFE
  • 2009-10. Combining seasonal time series ARIMA method and neural networks with genetic algorithms for predicting the production value of the mechanical industry in Taiwan in NEURAL COMPUTING AND APPLICATIONS
  • 1992. Genetic Algorithms + Data Structures = Evolution Programs in NONE
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  • Journal

    TITLE

    Neural Computing and Applications

    ISSUE

    8

    VOLUME

    21

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00521-011-0644-0

    DOI

    http://dx.doi.org/10.1007/s00521-011-0644-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1009390406


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