High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification.

基因表达数据的高维和样本不平衡促进了对基因表达数据进行分类的有效算法的发展。为了提高区分基因表达数据不同亚型的能力，我们设计了一种基于超量的离散进化优化算法（HYBDEOA）。同时优化了四个目标，即基因数量，准确性，相关性和冗余性，以指导进化。首先，二进制编码用于选择一些特征，将数据投影到不同的子空间。之后，进行离散邻域运算以生成新的二进制映射种群。将新的人口与当前的人口相结合，我们采用基于超容量的机制来选择Pareto解。最后，提出了一种离散变异方法，以在二分搜索空间中找到有希望的解决方案。为了证明HYBDEOA的性能，我们将HYBDEOA应用于55个合成数据集和35个癌症基因表达数据集。还进行了广泛的实验以揭示HYBDEOA的有效性和效率。实验结果表明，本文提出的方法是一种无参数，鲁棒的算法，可以对基因表达数据进行更好，更有益的分类。