Problem Decomposition Strategies and Credit Distribution Mechanisms in Modular Genetic Programming for Supervised Learning
Lino Rodriguez-Coayahuitl, Ansel Y. Rodríguez‐González, Daniel Fajardo‐Delgado, Maria Guadalupe Sánchez Cervantes · IEEE Transactions on Evolutionary Computation · 2025
In this review article, we provide a comprehensive guide to the endeavor of problem decomposition within the field of Genetic Programming (GP), specifically tree-based GP for supervised learning tasks. We analyzed in detail 70 manuscripts that deal with motifs such as “problem decomposition”“, modular GP”“, subroutine evolution”“, hierarchical GP”“, cooperative coevolution”, among others. As a result of this study, we propose an unifying taxonomy that categorizes efforts on problem decomposition in GP along three major axes: the architecture of evolved composite solutions, problem decomposition strategy, and credit assignment approach. This classification system sheds light on how the diverse proposed methodologies for problem decomposition relate to each other and where most of the research efforts have focused to this day. Rather than discussing in detail any particular set of works, we see this overview as a map that may help researchers in obtaining a wider view of existing efforts for problem decomposition in GP, as well as provide a cohesive framework that allows the disclosure of future developments in clearly differentiated niches. We close the article with a brief analysis that compares the current state of problem decomposition methodologies in GP with that of another exemplar of problem decomposition in machine learning: deep learning.