Explainable artificial intelligence by genetic programming: A survey

Y Mei, Q Chen, A Lensen, B Xue… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
Explainable artificial intelligence (XAI) has received great interest in the recent decade, due
to its importance in critical application domains, such as self-driving cars, law, and …

Genetic programming for production scheduling: a survey with a unified framework

S Nguyen, Y Mei, M Zhang - Complex & Intelligent Systems, 2017‏ - Springer
Genetic programming has been a powerful technique for automated design of production
scheduling heuristics. Many studies have shown that heuristics evolved by genetic …

Automated machine learning: past, present and future

M Baratchi, C Wang, S Limmer, JN van Rijn… - Artificial intelligence …, 2024‏ - Springer
Automated machine learning (AutoML) is a young research area aiming at making high-
performance machine learning techniques accessible to a broad set of users. This is …

Amlb: an automl benchmark

P Gijsbers, MLP Bueno, S Coors, E LeDell… - Journal of Machine …, 2024‏ - jmlr.org
Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …

Symbolic regression is NP-hard

M Virgolin, SP Pissis - arxiv preprint arxiv:2207.01018, 2022‏ - arxiv.org
Symbolic regression (SR) is the task of learning a model of data in the form of a
mathematical expression. By their nature, SR models have the potential to be accurate and …

A survey on optimization metaheuristics

I Boussaïd, J Lepagnot, P Siarry - Information sciences, 2013‏ - Elsevier
Metaheuristics are widely recognized as efficient approaches for many hard optimization
problems. This paper provides a survey of some of the main metaheuristics. It outlines the …

Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining …

W **ng, R Guo, E Petakovic, S Goggins - Computers in human behavior, 2015‏ - Elsevier
Building a student performance prediction model that is both practical and understandable
for users is a challenging task fraught with confounding factors to collect and measure. Most …

Evolutionary reinforcement learning: A survey

H Bai, R Cheng, Y ** - Intelligent Computing, 2023‏ - spj.science.org
Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge

LS Keren, A Liberzon, T Lazebnik - Scientific Reports, 2023‏ - nature.com
Discovering a meaningful symbolic expression that explains experimental data is a
fundamental challenge in many scientific fields. We present a novel, open-source …

Genetic programming in water resources engineering: A state-of-the-art review

AD Mehr, V Nourani, E Kahya, B Hrnjica, AMA Sattar… - Journal of …, 2018‏ - Elsevier
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …