High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray analysis by managing large and complex data systematically. However, combinatorial interactions among genes have not been considered as a unit of the analysis since previous methods mainly focus on a whole gene or a single isolated gene. Here, we introduce a molecular evolutionary algorithm called probabilistic library model (PLM). In the PLM, library elements are generated from gene combinations. An evolutionary procedure is adopted to learn the probabilistic distribution of training samples. We apply the PLM to prostate cancer microarray data. The experimental results show that the PLM classifiers perform better than conventional methods such as neural networks and decision trees in accuracy. We also examine the evolved library to find cancer-related gene combinations.