A General Variable Weighting Method for Threat Assessment Based on Genetic Algorithm

This paper designs a general variable weighting method for threat assessment based on a genetic algorithm. The method dynamically assigns weights to indicators in real-time within a constrained range according to the urgency level of each indicator. By defining the concept of indicator urgency, the indicators are categorized into nine levels of urgency. The genetic algorithm is used to search for the weight distribution within each variable range that most closely matches the urgency curve, thereby completing the variable weighting process. This method takes into account both the urgency of individual indicators and the relationships between the indicators. It can be used as an independent module and integrated with any threat assessment algorithm requiring prior weights, demonstrating broad applicability.