Unit Commitment is an important and vital optimization task in a power control centre. After load forecasting it is the second step in the planning process. It consists of two linked optimization problems. It comprises unit on/off scheduling problem and the economic dispatch sub-problem. The on/off scheduling problem is a 0-1 combinatorial problem with equality and inequality constraints, while the economic dispatch sub-problem is a nonlinear constrained optimization problem. Unit commitment is a nonlinear, large scale, combinatorial, constrained optimization problem. The complete unit commitment optimization problem is to minimize the total production cost (TPC) of utility in such a way that the constraints such as load demand, spinning reserve, minimum and maximum power limits of units, minimum up (MUT) and minimum down times (MDT) are satisfied. Therefore, based on the forecasted load demand, preparing proper on/off schedule of generators can result in cost saving for utility. It is much more difficult problem to solve due to its high dimensionality. The present work is based on the scheduling of thermal units. The generation of initial feasible UC schedules is much important, for the UCP. When initial feasible schedules (generation > load + spinning reserve) are generated randomly, it is difficult to get feasible schedules for the whole daily forecasted load curve. In this work initial schedule is generated by considering the peak, off peak load of the forecasted load curve, must run and must out units based on a new priority list method. The proposed method is very efficient and fast in generating initial unit commitment schedules. In this work the MUT and MDT constraints are checked and repaired by using bit change operator. The trial solutions were generated by taking upper four units in the priority list at each hour to avoid entrapment in local minimum.vi In the unit commitment problem, the economic load dispatch (ELD) sub-problem is an intensive part and its calculations consume a large amount of time. Convex economic dispatch using load to efficient unit and incremental cost criterion methods have been solved. In this work, the ELD calculation for non convex problem has been solved using genetic algorithm (GA) based on real power search method. In the present work, three hybrid approaches have been developed for the convex and non-convex cost functions and applied first time to solve the unit commitment problem. To implement these algorithms a flexible and extensible computational framework has been developed to run in visual C++ environment. The proposed algorithms are (i) “hybrid of dynamic programming, particle swarm optimization and artificial neural network algorithms (DP-PSO- ANN) for convex cost function, (ii) “Neuro-Genetic hybrid approach for non-convex cost function” and (iii) “hybrid of full load average production cost and maximum power output, for convex and non convex cost functions”. For comparison the neural network trained with back propagation learning rule has also been developed. The proposed models have been tested on IEEE 3 and 10 units standard test systems. The significant improvement in the total production cost shows the promise of these hybrid models. National utility system, National Transmission and despatch Company (NTDC) has been reviewed with reference to its operation problems. Four test systems consisting of 12, 15, 25 and 34 units of NTDC system have been tested.