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 …
[HTML][HTML] Hyper-heuristics: A survey and taxonomy
Hyper-heuristics are search techniques for selecting, generating, and sequencing (meta)-
heuristics to solve challenging optimization problems. They differ from traditional (meta) …
heuristics to solve challenging optimization problems. They differ from traditional (meta) …
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Discovering the underlying mathematical expressions describing a dataset is a core
challenge for artificial intelligence. This is the problem of $\textit {symbolic regression} …
challenge for artificial intelligence. This is the problem of $\textit {symbolic regression} …
Symbolic regression via neural-guided genetic programming population seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …
output from a black-box process. It is a discrete optimization problem generally believed to …
Symbolic regression via deep reinforcement learning enhanced genetic programming seeding
Symbolic regression is the process of identifying mathematical expressions that fit observed
output from a black-box process. It is a discrete optimization problem generally believed to …
output from a black-box process. It is a discrete optimization problem generally believed to …
Symformer: End-to-end symbolic regression using transformer-based architecture
Many real-world systems can be naturally described by mathematical formulas. The task of
automatically constructing formulas to fit observed data is called symbolic regression …
automatically constructing formulas to fit observed data is called symbolic regression …
A systematic literature review of the successors of “neuroevolution of augmenting topologies”
E Papavasileiou, J Cornelis… - Evolutionary …, 2021 - ieeexplore.ieee.org
NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks
(ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting …
(ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting …
From nodes to networks: Evolving recurrent neural networks
A Rawal, R Miikkulainen - arXiv preprint arXiv:1803.04439, 2018 - arxiv.org
Gated recurrent networks such as those composed of Long Short-Term Memory (LSTM)
nodes have recently been used to improve state of the art in many sequential processing …
nodes have recently been used to improve state of the art in many sequential processing …
Estimation of COVID-19 epidemic curves using genetic programming algorithm
This paper investigates the possibility of the implementation of Genetic Programming (GP)
algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models …
algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models …
Taylor genetic programming for symbolic regression
Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR)
problems. Compared with the machine learning or deep learning methods that depend on …
problems. Compared with the machine learning or deep learning methods that depend on …