Figure 2
The flowchart begins with setting algorithm parameters. Step 1 involves randomly generating an initial population representing different combinations of expert weights. Step 2 evaluates the fitness function (FF) by normalizing weights and applying Kendall's Coefficient. Step 3 tests the stop condition. If the condition is not met, the process proceeds to Step 4, where the population is updated through selection, crossover, and mutation. Step 5 involves correction before looping back to Step 2. The process ends when the stop condition is met.Genetic algorithm (GA) workflow for optimising expert weighting and consensus evaluation. Source: Authors’ own work