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 …

In order to minimize the generalization error in neural networks, a novel technique to identify
overfitting phenomena when training the learner is formally introduced. This enables support …

Bloat can be defined as an excess of code growth without a corresponding improvement in
fitness. This problem has been one of the most intensively studied subjects since the …

When learning from high-dimensional data for symbolic regression (SR), genetic
programming (GP) typically could not generalize well. Feature selection, as a data …

During virtual reality-based safety training, it is necessary to immediately and objectively
evaluate personal learning performance. In light of this, this study proposed an interpretable …

It is approximately 50 years since the first computational experiments were conducted in
what has become known today as the field of Genetic Programming (GP), twenty years since …

Nowadays, the field of human activity recognition (HAR) is a remarkably hot topic within the
scientific community. Given the low cost, ease of use and high accuracy of the sensors from …

Supervised learning by means of Genetic Programming (GP) aims at the evolutionary
synthesis of a model that achieves a balance between approximating the target function on …

Model complexity has a close relationship with the generalization ability and the
interpretability of the learned models. Simple models are more likely to generalize well and …

Many risk-sensitive applications require Machine Learning (ML) models to be interpretable.
Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyper …