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Outlier detection problem has important applications in the field of fraud detection, network robustness analysis and intrusion detection. Fundamental issue is that the notion of objects that are outliers typically varies between users, problems, domains or even datasets. Various researches have been carried out with different methods for outlier detection problems. The objective of the outlier detection problem is to find small groups of data objects that are exceptional when compared with rest large amount of data. In this paper, the authors propose Multi-populated parallel genetic algorithm for outlier detection problem. Testing on numerous examples from the related work indicates that MPGA is an appropriate tool for solving outlier detection problem.
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