# Applying Genetic Programming to Improve Interpretability in Machine Learning Models

arXiv: https://arxiv.org/abs/2005.09512 · HTML: https://mlsanity.com/paper/2005.09512/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 10 of 10)

- [Explainable Benchmarking for Iterative Optimization Heuristics](https://mlsanity.com/paper/2401.17842/) (2024)
- [Differentiable Genetic Programming for High-dimensional Symbolic Regression](https://mlsanity.com/paper/2304.08915/) (2023)
- [A Generic Approach for Reproducible Model Distillation](https://mlsanity.com/paper/2211.12631/) (2022)
- [Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set](https://mlsanity.com/paper/2404.05908/) (2024)
- [Evolutionary approaches to explainable machine learning](https://mlsanity.com/paper/2306.14786/) (2023)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [Exploring Hidden Semantics in Neural Networks with Symbolic Regression](https://mlsanity.com/paper/2204.10529/) (2022)
- [Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming](https://mlsanity.com/paper/2407.14714/) (2024)
- [Semantically Rich Local Dataset Generation for Explainable AI in Genomics](https://mlsanity.com/paper/2407.02984/) (2024)
- [EVO-LRP: Evolutionary Optimization of LRP for Interpretable Model Explanations](https://mlsanity.com/paper/2509.23585/) (2025)

## Abstract (arXiv)

Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, symbolic expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.