Incorporating A-Priori Expert Knowledge in Genetic Algorithms

Authors:
Mohammad-Reza Akbarzadeh-Totonchi;Mohammad Jamshidi
Affiliations:
-;-
Venue:
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Year:
1997

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Abstract

Conventional applications of GA 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 novel method is illustrated which incorporates a-priori knowledge in creating a fitter initial population while allowing for randomness among members of the population for diversity. Furthermore, the methodology is applied to optimization of a fuzzy controller's membership parameters in a water desalination control process, in particular a brine heater temperature control problem. It is shown that the GA-improved PID fuzzy controller is able to reduce overshoot by 80 percent when compared to non-GA PID fuzzy controller.