An Integrated Chaos Search Genetic Algorithm (CGA) /Fuzzy System (FS), Tabu Search (TS) and Neural Fuzzy Network (NFN) method for load forecasting is presented in this paper. A Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of Back-Propagation (BP). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. This thesis presents a hybrid Chaos Search Immune Algorithm (IA)/Genetic Algorithm (GA) and Fuzzy System (FS) method (CIGAFS) for solving short-term thermal generating unit commitment problems (UC). The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. We combined IA and GA, then added chaos search and fuzzy system approach in it. Then we used the hybrid system to solve UC. Numerical simulations were carried out using four cases; ten, twenty and thirty thermal units power systems over a 24-hour period.