Semantics-Driven Task Similarity in Multi-Task Genetic Programming for Multi-Output Symbolic Regression

Multi-Output symbolic regression predicts multiple target variables simultaneously, adding complexity overregression model with a single output, attributed to the interdependence between output variables. Effectively capturing these relationships is critical to improve prediction accuracy. Such a problem can be framed as a form of multi-task learning, where tasks share the same inputs but predict distinct output variables. This paper proposes a multi-task multi-population genetic programming algorithm that leverages the correlation between the distance matrices of the semantics of individuals in two populations to measure the similarity between the populations, thereby dynamically determining the similarity of two tasks. Furthermore, this precise similarity assessment guides a task selection strategy to determine the most appropriate task for knowledge transfer, ensuring efficient and effective transfer. Experiments on 18 real-world datasets demonstrate that the proposed method significantly enhances both training and test performance in multi-task GP, outperforming state-of-the-art methods on the majority of datasets.