Evolutionary optimization of fuzzy decision systems for automated insurance underwriting
2002
A robust method for automating the tuning and maintenance of fuzzy decision-making systems is presented. A configurable multi-stage mutation-based evolutionary algorithm optimally tunes the decision thresholds and internal parameters of fuzzy rule-based and case-based systems that decide the risk categories of insurance applications. The tunable parameters have a critical impact on the coverage and accuracy of. decision-making, and a reliable method to optimally tune these parameters is critical to the quality of decision-making and maintainability of these systems.
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