A weaknesses of the genetic algorithm (GA) is when GA traps to a local optimum and unable\r\nto escape, so its performance continues to deteriorate. One of the methods to overcome these weaknesses\r\nis hybrid genetic algorithm with adaptive local search scheme (HGA). This paper present application of\r\nHGA to effectively solve the traveling salesman problem. This application was developed on android\r\nbecause android is now widely used around the world and it is mobile system. The use of local search\r\ntechnique to search for a better solution in the neighborhood. If it finds a better solution, it changes the\r\ncurrent solution GA with this new one. For local search scheme that can automatically control the use of\r\nlocal search technique into GA so that local search is adaptive to the GA. The best solution is generated\r\nby the algorithm shown in google maps on android. In the experiment, to test the effectiveness of the\r\nHGA is compare with GA in 5 sample from the cities in Central Java, Indonesia with different numbers of\r\ncities. According to the experiment results obtained that in 3 tests out of 5 (60%), HGA found the optimal\r\nsolutions and in 2 test (40%), found the same with the best solution of GA. The worst solution and the\r\naverage solution HGA shows in 5 tests out of 5 (100%) is better than GA. The results have shown that the\r\nhybrid genetic algorithm outperforms the genetic algorithm especially in the case with the problem higher\r\ncomplexity.

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