In recent years, evolution-based knowledge optimization has gained a great deal of popularity due to its inherent ability in efficient and parallel search of complex and multimodal landscapes. Application of genetic algorithms (GA) to knowledge enhancement involves several aspects. First is how to code a string to represent all the necessary degrees of freedom for search in the fuzzy knowledge domain. The second aspect is how to incorporate existing expert knowledge into the GA-optimising algorithm. And in general, how to take advantage of several experts' opinions in creation of an initial population. Conventional applications of GA-fuzzy suggest using a random initial population. However, it is intuitively clear that any search routine could converge faster if starting points are good solutions. In this paper, a methodology is illustrated which incorporates expert knowledge in creating an initial population while allowing for randomness among members of the population for diversity. Furthermore, the methodology is applied to step response optimization of a flexible-link feedback control system.