Spatially Distributed Learning

doi:10.1007/978-3-032-09037-9_1

Spatially Distributed Learning

2026-01-01

0

PRE

AI

L

Liván García Lache

L

Lino Alberto Rodríguez Coayahuitl *

R

Rodriguez Gonzalez, Ansel Yoan

DOI：10.1007/978-3-032-09037-9_1

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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.

Keywords:

Genetic Programming

Computational Time

Population Evolution

Data Subsets

Spatially Distributed Learning

Spatially Distributed Learning

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