Problem Decomposition Strategies and Credit Distribution Mechanisms in Modular Genetic Programming for Supervised Learning

Abstract

In this review article, we provide a comprehensive guide to the endeavor of problem decomposition (PD) 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 “PD,” “modular GP,” “subroutine evolution,” “hierarchical GP,” “cooperative coevolution,” among others. As a result of this study, we propose a unifying taxonomy that categorizes efforts on PD in GP along three major axes: 1) the architecture of evolved composite solutions; 2) PD strategy; and 3) credit assignment approach. This classification system sheds light on how the diverse proposed methodologies for PD 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 PD in GP, as well as provide a cohesive framework that allows the disclosure of future developments in clearly differentiated niches. We close this article with a brief analysis that compares the current state of PD methodologies in GP with that of another exemplar of PD in machine learning: deep learning.