Abstract
Genetic algorithm has an important role in improving clustering methods. When it comes to getting into a dataset, one of the most common methods used is clustering. Recent years have seen a rise in the number of articles interested in clustering, which may be attributed to the development of various new fields of application. Most clustering method's performance depends on initial values of parameters or the preprocessing of a dataset, so clustering needs to optimize. The improvement of clustering in two directions, either we improve the parameters or improve the input data. Ga is an evolutionary technique well-known in optimization. This survey deals with the methods that use a genetic algorithm to choose parameter values and input data. This study shows many important performance metrics used to get the optimal result in each research.
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