Evolutionary optimization of fuzzy decision systems for automated insurance underwriting

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