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Unit commitment is a large-scale short-term optimization problem, in which the main objective is to schedule generation to minimise the total fuel cost or to maximise the total profit over a study period, subject to a large number constraints that must be satisfied. There exists no exact solution technique with a reasonable computation time to provide optimum solution to the unit commitment problem. In large systems, the problem becomes increasingly complex due to the enormous number of possible combinations of the on and off states of all the generating units in the power system over all the time-points in the study period. As the power industry undergoes radical restructuring, the value of the improved solutions that today's optimization algorithms might yield for this problem is increasing. This paper presents an efficient algorithm for aiding unit commitment decisions in such environments. An evolutionary algorithm (EA) with problem specific heuristics and genetic operators has been employed to solve the problem. The initial random population is seeded with good solutions using a priority list method to increase the speed of convergence and improve efficiency of the algorithm. Test results on systems of varying sizes show superiority of this approach compared to other methods reported in the literature.