Efficiency Enhancement of Standard Genetic Algorithm using One Dimensional Cellular Automate
Subject Areas : Information Technology in Engineering Design (ITED) Journal
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Abstract: Genetic Algorithm is a powerful algorithm for solving optimization problems. Its performance is affected by appropriate selection of its parameters and operators such as size of population, type of crossover, mutation, etc. Different methods have been suggested to enhance the performance of standard Genetic Algorithm (sGA). In this paper, we propose a new method based on one dimensional Cellular Automata (1D CA) to enhance the performance of sGA. The proposed hybrid CA-GA algorithm then has been used to find the minimum of five well-known test functions (with 5 and 10 dimension). Results showed that the hybrid CA-GA algorithm has a greater accuracy compared with sGA in finding the best minimum of test function. Also, the convergence speed of hybrid CA-GA algorithm to the exact global minimum is clearly more than sGA.
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