Semantics-guided multi-task genetic programming for multi-output regression

Multi-output regression entails the simultaneous prediction of two or more output variables, presenting greater complexities than single-output regression due to the frequent interdependent relationships of these variables. Such dependencies mean that accurately predicting one variable typically requires careful analysis of its relationships with others. In this paper, multi-output regression problems are treated as multi-task problems, with a prediction of one output variable as a distinct task. A new multi-task multi-population genetic programming method is proposed to solve the problem. The method incorporates a semantics based crossover operator to identify the most informative subtree from a similar task that facilitates positive knowledge transfer. Empirical results indicate that our method significantly improves the training and testing performances of other multi-task GP methods, surpassing standard GP and GP with regressor chain on most examined regression datasets. Further analysis reveals that our proposed method can generate high-quality solutions by knowledge transfer and efficiently evolves similar GP models for analogous output variables, significantly enhancing positive knowledge transfer. • A semantics based crossover operator identifies the most informative subtree to transfer knowledge. • An origin based reservation strategy maintains structures to ensure a high-quality population. • Experiments show the proposed method improves learning efficiency and generalization of multi-task GP.