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The paper examines various applicability issues of the negative selection algorithms (NSA). Recently, concerns were raised on the use of NSAs, especially those using real-valued representation. In this paper, we argued that many reported issues are either due to improper usage of the method or general difficulties which are not specific to negative selection algorithms. On the contrary, the experiments with synthetic data and well-known real-world data show that NSAs have great flexibility to balance between efficiency and robustness, and to accommodate domain-oriented elements in the method, e.g. various distance measures. It is to be noted that all methods are not suitable for all datasets and data representation plays a major role.