Jumping gene genetic algorithm and its applications in controller design

In this paper, two newly-designed jumping gene (JG) operations are proposed for the enhancement of genetic algorithm (GA). Based on the recent findings on their schemata growth rates, it is recognized that JG can provide the essential diversity in a GA so as to greatly enhance its searching ability. This nature is particularly important for multi-objective problem, in which a set of Pareto solutions is expected. As demonstrated by its application on the design of Fuzzy PID controllers, JGGA is considered as an effective and reliable methodology for multi-objective designs.