Automatic analysis of medical images using machine learning techniques has gained
significant importance over the years. A large number of approaches have been proposed …

A variable reduction strategy (VRS) is an effective method to accelerate the optimization
process of evolutionary algorithms (EAs) by simplifying the corresponding optimization …

Explainable artificial intelligence (XAI) is an important and rapidly expanding research topic.
The goal of XAI is to gain trust in a machine learning (ML) model through clear insights into …

Gray-box optimization employs the knowledge about the true direct gene dependencies
represented by the Variable Interaction Graph (VIG). This knowledge is utilized in many …

Exploiting knowledge about the structure of a problem can greatly benefit the efficiency and
scalability of an Evolutionary Algorithm (EA). Model-Based EAs (MBEAs) are capable of …

The objective value of an ill-conditioned function may significantly change with a minor shift
of the argument in the search space. Ill-conditioned functions do not have at all or exhibit …

Traditional approaches to neuroevolution often start from scratch. This becomes prohibitively
expensive in terms of computational and data requirements when targeting modern, deep …

Machine learning models can be made more performant and their predictions more
consistent by creating an ensemble. Each neural network in an ensemble commonly …

Locality is a crucial property for efficiently optimising black-box problems with randomized
search heuristics. However, in practical applications, it is not likely to always find such a …

Portfolio optimization of cryptocurrencies with Evolutionary Algorithms is a fairly new topic in
financial literature. New and upcoming studies are addressing portfolio optimization …