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This paper presents a genetic algorithm (GA) approach for solving the generators scheduling problem and modifications are adopted to improve the performance of the GA for the scheduling problem. Solving the problem is able to determine a start-up and shut-down schedule for all available generators in a power system over a period of time to meet the forecasted load demand at minimum cost. From the schedule, near optimum production cost is achieved while satisfying a large set of operating constraints. The operating constraints including fuel cost, start-up costs, minimum start-up time and shutdown time must be met for the generators schedule. Owing to the nonconvex and combinatorial nature of the scheduling problem, conventional programming methods are difficult to solve this problem. However, the application of GA is suitable for solving the problem. GAs are adaptive search techniques to determine the global optimal solution of a combinatorial optimization problem which are based on the mechanics of natural genetics and natural selection.