The rapid developments of technologies that generate arrays of gene data enable a global view of the transcription levels of hundreds of thousands of genes simultaneously. The outlier detection problem for gene data has its importance but together with the difficulty of high dimensionality. The sparsity of data in high-dimensional space makes each point a relatively good outlier in the view of traditional distance-based definitions. Thus, finding outliers in high dimensional data is more complex. In this paper, some basic outlier analysis algorithms are discussed and a new genetic algorithm is presented. This algorithm is to find best dimension projections based on a revised cell-based algorithm and to give explanations to solutions. It can solve the outlier detection problem for gene expression data and for other high dimensional data as well.