In this study, an improved genetic algorithm is proposed for optimal sensor placement in structures. This algorithm is established by a numerical forward algorithm based on a geometrical viewpoint and genetic algorithm (GVGA). The aim of this strategy is to minimize the effects of noise on the process of optimization algorithm. For this purpose, the fitness function is considered as the standard deviation of the diameters of elliptical noise in the response change space. It is acquired using the geometrical viewpoint. The algorithm is applied to the space structures, then the optimal sensor placement obtained by the GVGA and the GA are compared. The results show that the GVGA improves the algorithm's convergence resulting in a better sensor pattern.
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