A Comparative Study for Data Approximation Between Two Explainable Artificial Intelligence Approaches

Recently, finding the mathematical equations that match with data from any function has been considered a significant challenge for artificial intelligence and is known as symbolic regression. In a nutshell, symbolic regression is a subset of regression analysis that uses mathematical equation space to look for the best paradigm that matches the data and thus can match a much broader range of data sets than other paradigms, like linear regression. Explainable artificial intelligence has recently appeared where symbolic regression methods have been used for a long time to build models that are both understandable and tractable mathematically. Two symbolic regression methods, a network operator (NOP) and cartesian genetic programming (CGP), are discussed in detail. This study presents approaches for coding a mathematical equation and the basic collections of elementary functions that must be generated to perform this task. A comparative study for solving classical symbolic regression equations (benchmarks) has been carried out between the network operator method and cartesian genetic programming. It has been demonstrated through numerical results that the network operator outperforms cartesian genetic programming. © 2024 American Institute of Physics Inc.. All rights reserved.