The genetic algorithm searches for an optimal solution using the principles of evolution based on a given string which is judged and propagated to form the next generation. The algorithm is designed so that the “best-fitting” strings survive and propagate into subsequent generations. The genetic algorithm has been reported to produce superior results because it has the ability to search for a near-global optimal solution. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The theoretical foundations of genetic algorithms (GA) were first described by John Holland and then presented by David Goldberg. G. Boone and H. Chiang devised a GA-based method for determining optimal capacitor sizes and locations. The sizes and positions of the capacitors are encoded into binary strings and a crossover is performed to generate a new population. This problem formulation only considers capacitor costs and peak power loss reduction. S. Sundhararajan and A. Pahwa proposed an optimization method using genetic algorithm to determine the optimal selection of capacitors. However, their work differs in that they use an elitist strategy; therefore the encoded strings chosen for the next generation do not undergo mutation or crossover procedures. Furthermore, this formulation includes the reduction of energy losses which has been omitted. K. Miu, H. Chiang, and G. Darling revisited the GA formulation and included additional capacitor replacement and control capabilities for unbalanced distribution systems. Kim and S. You used the genetic algorithm to obtain the optimal shunt capacitor bank values. They treated the capacitors as loads of constant reactive power. M. Delfanti et al. present a procedure to solve the capacitor placement problem. The objective is to determine the minimum investment required to satisfy adequate reactive constraints. D. Das presents the optimal locations for capacitors under varying load levels using GA to minimize energy loss while maintaining the voltage on the load buses within the specified limit taking into account the cost of the capacitors. S. Karaki et al. presented an efficient method to determine the optimal number, location, and sizing of fixed and switched shunt capacitors in radial distribution systems using GA.K. Kim et al. in proposed a hybrid simplex GA approach combined with multi-population GA to determine the location, size and number of capacitors in unbalanced distribution systems, although harmonic distorted systems were not considered in this study. Z. Hu et al. they used GA for offline VVO to minimize energy losses. In this case the switching operation of OLTC was limited by the time slot approach, so the search space for GA was reduced. M. Masoum et al. reported a GA-based method that incorporates nonlinear load models for the problem of finding optimal shunt capacitors on distribution systems. R. Santos et al. proposed a nested procedure to solve the problem of optimal capacitor placement for distribution networks. At an external level, a small genetic algorithm is adopted aimed at maximizing the net profit associated with the investment in capacitor banks.B. Milosevic and M. Begovic proposed a strategy based on non-ordering genetic algorithm for the optimal allocation of capacitors in the distribution system to minimize the losses of the.