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Research on Genetic Algorithms (GAs) has shown that the initial proposals are incapable of solving hard problems in a robust and efficient way. Usually, for large-scale optimization problems, the execution time of first-generation GAs increases dramatically whereas solution quality decreases. The aim of this paper is to point out the main design issues in tailoring Simulated Annealing and GAs to large-scale optimization problems. In present paper, an objective function is defined with constraints and solved by both the technique i.e., SA and GA. The solution of this problem has shown the superior performance of SA as compared to GA in optimization technique.
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