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.
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