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In this work, a new method is proposed to accelerate the evolution of Genetic Programming populations. The proposed method replaces the evaluation of each individual on the entire dataset by dividing the training data into small subsets and assigning each individual in the population a different subset, in order to reduce computational time. The performance of the proposed approach has been examined on 7 datasets, 4 for classification and 3 for regression, with a variety of difficulties in both groups. The results obtained demonstrate that the new approach achieves performance equivalent to the conventional algorithm of training all individuals on the entire dataset; this procedure allows for a significant reduction in computation time compared to the classical GP algorithm against which it was compared.

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Spatially Distributed Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Liván García Lache,
  • Lino Alberto Rodríguez Coayahuitl,
  • Ansel Yoan Rodríguez González

摘要

In this work, a new method is proposed to accelerate the evolution of Genetic Programming populations. The proposed method replaces the evaluation of each individual on the entire dataset by dividing the training data into small subsets and assigning each individual in the population a different subset, in order to reduce computational time. The performance of the proposed approach has been examined on 7 datasets, 4 for classification and 3 for regression, with a variety of difficulties in both groups. The results obtained demonstrate that the new approach achieves performance equivalent to the conventional algorithm of training all individuals on the entire dataset; this procedure allows for a significant reduction in computation time compared to the classical GP algorithm against which it was compared.