Multi-Target Regression problem comprises the prediction of multiple continuous variables given a common set of input features, unlike traditional regression tasks, where just one output target is available. There are two major challenges when addressing this problem, namely the exploration of the inter-target dependencies and the modeling of complex input–output relationships. This work proposes a Symbolic Regression method following the basis of Gene Expression Programming paradigm to solve the multi-target regression problem, and called GEPMTR. It evolves a population of individuals, where each one represents a complete solution to the problem by using a multi-genic chromosome, and encodes a mathematical function for each target variable involving the input attributes. The proposed model can estimate the inter-target dependencies by applying some genetic operators. Furthermore, three ensemble-based methods are developed to better exploit the inter-target and input–output relationships. The effectiveness of the proposals is analyzed through an extensive experimental study on 18 datasets. The codification schema and the process followed to ensure a diverse population in GEPMTR lead to obtain an effective proposal to solve the MTR problem. Furthermore, the EGEPMTR-B ensemble method obtained the best performance across all proposed models, being the best in 8 out of 11 cases, demonstrating that more sophisticated mechanisms were not needed for ensuring that GEPMTR method would properly model the existing inter-target dependencies. Finally, the experimental results also showed that the proposed approach attains competitive results compared to state-of-the-art, showing the possibilities that can bring this research line for effectively solving the MTR problem.

多目标回归问题包括在给定一组公共输入特征的情况下对多个连续变量的预测，这与传统的回归任务不同，在传统的回归任务中，只有一个输出目标可用。解决此问题时，存在两个主要挑战，即对目标间依存关系的探索和对复杂的输入-输出关系的建模。这项工作提出了一种基于基因表达编程范例的符号回归方法来解决多目标回归问题，称为GEPMTR。它演化出一群个体，其中的每个个体都使用多基因染色体代表了问题的完整解决方案，并为涉及输入属性的每个目标变量编码了数学函数。所提出的模型可以通过应用一些遗传算子来估计目标间的依赖性。此外，开发了三种基于集成的方法，以更好地利用目标之间和输入输出之间的关系。通过对18个数据集进行广泛的实验研究，分析了提案的有效性。为确保GEPMTR中的人口多样化而进行的编纂方案和程序，导致获得了解决地铁问题的有效建议。此外，EGEPMTR-B集成方法在所有提出的模型中均获得了最佳性能，在11种情况中有8种是最佳的，这表明不需要更复杂的机制来确保GEPMTR方法可以正确地建模现有目标间依赖关系。最后，