Power Economic Dispatch (ED) is necessary and vital step in power system operational planning. It is nonconvex constrained optimization problem defined as the process of calculating the generation of the generating units for the minimum total production cost in such a way that both equality and inequality constraints are satisfied. In system operation studies generators are represented by input-output curves. These characteristics curves are inherently nonlinear and non-smooth due to valve point effect, multiple fuels and operational constraints such as prohibited operating zones. The accurate economic dispatch depends mainly upon the accurate representation of these curves and their handling in the optimization process. Generally, economic dispatch is formulated as convex problem and has been solved using mathematical programming techniques by approximating generator input/output characteristic curves of monotonically increasing nature thus resulting in an inaccurate dispatch. However, the nonconvex ED problem cannot be handled effectively by such approaches. The Genetic algorithm is the potential solution methodology due to its inherent ability to address the convex and nonconvex problems equally. This dissertation presents the application of genetic algorithm (GA) for the solution of economic dispatch problem independently as well as in hybrid form in conjunction with the other techniques. The problem is addressed first by developing a extensible and flexible computational framework called “PED_Frame” as common environment which becomes a platform for the computer implementation of different algorithms under consideration. This framework has been used for implementation of economic dispatch algorithms for (i) GA based models, (ii) Hybrid models. Economic dispatch problem has been formulated in binary coded genetic algorithm environment based on real power search and λ search methodologies. Two biological mechanism “inversion” and “deletion-regeneration” has also been mapped as an operator with crossover probability. Various GA based evolution models have been constructed by adopting different initial population generation schemes, selectionvi methods, and crossover operators. Convex ED studies have been conducted using standard test systems and results have been compared with λ iteration approach. GA based hybrid approach for convex ED dispatch is proposed. This approach initially run GA based ED with λ-search and passes the control to conventional λ iteration technique. This approach gives another systematic method for selection of initial value of λ. The results of the proposed approach on standard test system show that costs of generation by this approach is almost the same as the λ iteration alone, however, it takes less number of iterations. The performance of GA based economic dispatch problem has been evaluated with reference to different evolution models on the basis of empirical data available by actually running the program for the nonconvex ED due to valve point effect. National utility system has been reviewed with reference to its operation problems. Four test systems close to original network have been developed and tested by load flow analysis using Newton’s Raphson algorithm. Finally 12-Machine 32 bus test circuit, 15, 25 and 34 Machines systems for economic dispatch studies have been developed. ED studies have been conducted using test circuit The Genetic algorithm has the inherent ability to bring the solution to the global minimum region of search space in a short time and then takes longer time to converge to the solution. This research work proposed hybrid approaches to fine tune the near optimal results produced by GA. In this context, three hybrid approaches have been used for the solution of nonconvex economic dispatch problem with valve point effect. These include (i) A Synergy of GA and ED using Newton’s Second Order Approach, (ii) Neuro-Genetic Hybrid Approach, and (iii) Hybrid of GA and Sequential Quadratic Programming. These models have been tested on standard test systems and the results obtained from all the three hybrid approaches offer significant improvement in the generation cost showing the promise of the proposed approaches.