Applying Genetic Programming to Improve Interpretability in Machine Learning Models

18 May 2020

Papers citing "Applying Genetic Programming to Improve Interpretability in Machine Learning Models"

10 / 10 papers shown

Title
Unveiling the Decision-Making Process in Reinforcement Learning with Genetic Programming Manuel Eberhardinger Florian Rupp Johannes Maucher S. Maghsudi 38 0 0 20 Jul 2024
Semantically Rich Local Dataset Generation for Explainable AI in Genomics Pedro Barbosa Rosina Savisaar Alcides Fonseca 14 0 0 03 Jul 2024
Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems Ryan Zhou Jaume Bacardit Alexander Brownlee Stefano Cagnoni Martin Fyvie Giovanni Iacca John Mccall Niki van Stein David Walker Ting-Kuei Hu 44 0 0 12 Jun 2024
Interpretability in Symbolic Regression: a benchmark of Explanatory Methods using the Feynman data set Guilherme Seidyo Imai Aldeia Fabrício Olivetti de França 37 10 0 08 Apr 2024
Function Class Learning with Genetic Programming: Towards Explainable Meta Learning for Tumor Growth Functionals E. Sijben Jeroen Jansen Peter A. N. Bosman Tanja Alderliesten 34 1 0 19 Feb 2024
Explainable Benchmarking for Iterative Optimization Heuristics Niki van Stein Diederick Vermetten Anna V. Kononova Thomas Bäck 37 12 0 31 Jan 2024
Evolutionary approaches to explainable machine learning Ryan Zhou Ting-Kuei Hu 35 7 0 23 Jun 2023
Differentiable Genetic Programming for High-dimensional Symbolic Regression Peng Zeng Xiaotian Song Andrew Lensen Yuwei Ou Yanan Sun Mengjie Zhang Jiancheng Lv 39 2 0 18 Apr 2023
Exploring Hidden Semantics in Neural Networks with Symbolic Regression Yuanzhen Luo Qiang Lu Xilei Hu Jake Luo Zhiguang Wang 13 0 0 22 Apr 2022
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models E. Sijben Tanja Alderliesten Peter A. N. Bosman 21 5 0 24 Mar 2022