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This paper presents the optimal tuning of power system stabilizer parameters using a newly introduced evolutionary algorithm called Population Based Incremental Learning (PBIL). To robustly stabilize the system, an objective function that minimizes the infinity norm of the closed-loop system is introduced such that the parameters of a fixed structure PSS are optimally tuned and the controller stabilizes a pre-specified set of system models. The PBIL-PSS is compared with the Conventional PSS (CPSS). The simulation results presented in this paper show that the proposed PBILPSS is more effective than the Conventional PSS in damping the low frequency oscillations. The performance of the proposed PSS is also evaluated using the Real Time Digital Simulator (RTDS). The experimental results obtained from the RTDS confirm the proposed controller is robust for under small disturbance.