Delima Sitanggang


In classical genetic algorithm, the determination of the initial individu generated by random methods. In the study using a large individu, these methods often cause undesirable effects such as premature convergence in finding the optimal solution. In this study, algorithm Simple Hill Climbing (SHC) as the algorithm locally optimal analyzed its application to improve the performance of the genetic algorithm in order to avoid the genetic algorithm to the problem of convergence premature so expect to achieve optimal solutions in solving the Traveling Salesman Problem (TSP), In this research, three types of experiments by applying different parameters of Genetic Algorithm.In the first experiment, initial values obtained for the solution is 3596.6, Genetic Algorithm In the second experimental values obtained initial solution to SHC at 3494.1, and the best SHC for the best solution Genetic Algorithm In the third experiment obtained the initial value of 3330.9

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