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Explainable artificial intelligence by genetic programming: A survey
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 …
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
Genetic programming has been a powerful technique for automated design of production
scheduling heuristics. Many studies have shown that heuristics evolved by genetic …
scheduling heuristics. Many studies have shown that heuristics evolved by genetic …
Automated machine learning: past, present and future
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 …
performance machine learning techniques accessible to a broad set of users. This is …
Amlb: an automl benchmark
Comparing different AutoML frameworks is notoriously challenging and often done
incorrectly. We introduce an open and extensible benchmark that follows best practices and …
incorrectly. We introduce an open and extensible benchmark that follows best practices and …
Symbolic regression is NP-hard
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 …
mathematical expression. By their nature, SR models have the potential to be accurate and …
A survey on optimization metaheuristics
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 …
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 …
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 …
for users is a challenging task fraught with confounding factors to collect and measure. Most …
Evolutionary reinforcement learning: A survey
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 …
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
Discovering a meaningful symbolic expression that explains experimental data is a
fundamental challenge in many scientific fields. We present a novel, open-source …
fundamental challenge in many scientific fields. We present a novel, open-source …
Genetic programming in water resources engineering: A state-of-the-art review
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 …
automatic generation of computer programs. In recent decades, GP has been frequently …