Due to the existence of systematic bias in the measurements, intermicroarray normalization is required before the integration data analysis of multiple microarray datasets. The current intermicroarray normalization methods rely on some assumptions, which rarely can be verified by experiments, such as the invariant expression level of housekeeping genes or the stability of statistical data distribution. In this paper, we present a highly verifiable intermicroarray normalization method using a genetic optimization algorithm, through which only a small number of housekeeping genes are selected as the normalization markers to facilitate experiment validation without any precision lost.