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 …

Traffic assignment is of great importance in real life from foot traffic assignment of a building
to vehicle traffic assignment of a city. With the rapid increase of the number of agents and the …

Finding a concise and interpretable mathematical formula that accurately describes the
relationship between each variable and the predicted value in the data is a crucial task in …

Estimation of distribution programming is an iterative method to evolve program trees. It
estimates the distribution of the most suitable program trees and then produces a new …

Abstract Genetic Programming (GP) based Symbolic Regression (SR) algorithms suffer from
the ineluctable effects over model bloat, blind search and diversity loss when determining …

Modularity is a ubiquitous principle that permeates various aspects of nature, society, and
human endeavors, from biological systems to organizational structures and beyond. In the …

Data incompleteness is a serious challenge in real-world machine-learning tasks.
Nevertheless, it has not received enough attention in symbolic regression (SR). Data …

Searching for symbolic models plays an important role in a wide range of domains such as
neural architecture search and automatic program synthesis. Genetic programming is a …

Genetic programming (GP) is a widely recognized and powerful approach for symbolic
regression (SR) problems. However, existing GP methods rely on a single form to solve the …

When solving optimization problems with expensive or implicit objective functions,
evolutionary algorithms commonly utilize surrogate models as cost-effective substitutes for …