Outlier, abnormal or unusual observation can be defined as an observation that lies outside the overall pattern of a distribution. Diagnostic methods for identifying a single outlier or influential observation in a linear regression model are relatively simple from both analytical and computational points of view. However, if the data set contains more than one outlier, which is likely to be the case in most data sets, the problem of identifying such observations becomes more difficult because of the masking and swamping effects. A GA was allowed simultaneous detection of outliers in data sets. Thus, this method is to overcome the problems of masking and swamping effects. It is derived additional penalized value of information criteria for Akaike Information Criterion (AIC) and Information Complexity Criterion (ICOMP) and named as AIC'' and ICOMP'' respectively in this study. The numerical example and simulation results clearly show a much improved performance of the proposed approach in comparison to existing method especially followed by applying the ICOMP'' approach in order to accurately (robustly) detect the outliers.