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The researches on driver pathway identification in cancer rely on traditional biological experiments, which have the drawbacks of being time-consuming, labor-intensive and costly. This paper proposed a novel binary cancer driver pathway identification method called PEA-BLMWS(parental evolutionary algorithm-binary linear maximum weight sub-matrix). Firstly, it utilized the existing gene expression data to uncovered potential gene mutation data by comparing the differences in expression levels between normal and mutated genes. Secondly, it incorporated protein-protein interaction network data to construct an improved binary linear maximum weight sub-matrix model. Finally, it proposed a parental evolutionary algorithm to solve this matrix model. Experimental results on the GBM(glioblastoma) and OVCA(ovarian cancer) datasets show that compared to other advanced identification methods such as Dendrix, CCA-NMWS and CGP-NCM, the gene set identified by PEA-BLMWS has more genes enriched in known signaling pathways, and genes not enriched in signaling pathways are also closely related to the occurrence of cancer. Therefore, this identification method can serve as an effective tool for driving pathway identification. [ABSTRACT FROM AUTHOR]