Genetic algorithms (GAs) are adaptive procedures that find solution to problems by an evolutionary process that mimics natural selection. In this paper, methods based on GAs have been developed and presented for design optimization of journal bearings in nonlinear rotor system. The GA uses a 30-bit chromosome to represent the bearing radial clearance, aspect ratio and lubricant viscosity, with 10-bit for each design variable. The instability onset speed of the system is taken as the fitness function in GA, in which the nonlinear effects of the bearing fluid forces are considered. The instability onset speed is defined in two different cases, that is, periodic and quasi-periodic or chaotic motions. To verify the effectiveness of the suggested method, a rigid rotor-bearing system is taken as an example to be optimized. The different crossover probabilities, mutation probabilities and population sizes are employed to analyze their influences on GA so that a set of appropriate parameters are chosen to be used in the final calculation. The results are compared with those obtained by numerical simulation. It is shown that the proposed algorithm is effective in the optimum design of rotor-bearing system.

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