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The Job-Shop Scheduling Problem (JSSP) is well known as one of the most difficult NP-hard combinatorial optimization problems. Several GA-based approaches have been reported for the JSSP. Among them, there is a parameter-free genetic algorithm (PfGA) for JSSP proposed by Matsui et al., based on an extended version of PfGA, which uses random keys for representing permutation of operations in jobs, and uses a hybrid scheduling for decoding a permutation into a schedule. They reported that their algorithm performs well for typical benchmark problems, but the experiments were limited to a small number of problem instances. This paper shows the results of an empirical performance evaluation of the GA for a wider range of problem instances. The results show that the GA performs well for many problem instances, and the performance can be improved greatly by increasing the number of subpopulations in the parallel distributed version.