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

Content Provider	Springer Nature Link
Author	Su, Chao Ton Wang, Fang Fang
Copyright Year	2011
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.
Starting Page	2127
Ending Page	2135
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	21
Issue Number	8
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-05-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy connectives Hierarchical aggregation networks Genetic algorithms (GAs) Decision analysis Quality of life Computational Biology/Bioinformatics Computational Science and Engineering Image Processing and Computer Vision Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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