Rank Based Genetic Algorithm for solving the Banking ATM's Location Problem using convolution

In order t o satisfy the client needs, his Utility should be increased by covering his Demand. The service Utility should be maximized through effective deployment of ATMs. Genetic Algorithm is one of widely used techniques to solve complex optimization problems, such as Banking ATM's Location Problem. This paper proposes a novel Rank Based Genetic Algorithm using convolution for solving the Banking ATM's Location Problem (RGAC). The proposed RGAC maximizes demand Coverage Percentage with less number of ATM machines. The novel RGAC speeds up the convergence using Rank Concept, with limited number of iterations to obtain a high quality feasible solution in resonable time. The proposed algorithm RGAC performs more effectively in the large scale deployments, thus it can be used in the marketing study of Banks which have highly complex operations. The simulation results show that RGAC improves the Percentage Coverage up to 16.2 over the previous algorithm using the same number of ATMs. Also they exhibit that RGAC reduces the number of ATM machines up to ten (10).