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This paper presents a new approach for solving the unit commitment problem (UCP) in hydro-thermal power system. The main objective of this paper is to find the generation scheduling by committing the generating units such that the total operating cost can be minimized by satisfying both the forecasted load demand and various operating constraints. It is a Global optimization technique for solving UCP, operates on a system, which is designed to encode each unit’s operating schedule with regard to its minimum up/down time. In this method, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here the parents are obtained from a pre-defined set of solutions that is, each and every solution is adjusted to meet the requirements. Then, random recommitment is carried out with respect to the unit’s minimum down times. Tabu search (TS) is a powerful optimization procedure that has been successfully applied to a number of combinatorial optimization problems. It avoids entrapment at local optimum by maintaining a short term memory of recently obtained solutions. The memory structure assists in forbidding certain moves that deteriorates the quality of the solution by assigning Tabu status to the forbidden solutions. The Tabu status of a solution can be overruled if certain conditions are satisfied expressed in the form of aspiration level (AL). AL adds flexibility in TS by directing the search towards attractive moves. The best population is selected by evolutionary strategy (ES). Numerical results are shown comparing the cost solutions and computation time obtained by using the proposed hybrid method with conventional methods like Dynamic Programming, and Lagrangian Relaxation etc.	 	Key words: Evolutionary programming, Tabu search, unit commitment, dynamic programming, lagrangian relaxation.

An evolutionary programming embedded Tabu search method for hydro-thermal scheduling with cooling banking constraints