Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

A bilevel programming model represents the relationship in a specific decision process that
involves decisions within a hierarchical structure of two levels. The upper-level problem is …

Reducing the number of selected features and improving the classification performance are
two major objectives in feature selection, which can be viewed as a multi-objective …

The automatic design of soft robots characterizes as jointly optimizing structure and control.
As reinforcement learning is gradually used to optimize control, the time-consuming …

Minimizing the number of selected features and maximizing the classification performance
are two main objectives in feature selection, which can be formulated as a bi-objective …

Abstract Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and
engineering design problems are fundamental to the development of artificial intelligence …

Abstract The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm
leveraging swarm intelligence to tackle real-world optimization problems. However, when …

This chapter introduces the use of Evolutionary Machine Learning (EML) techniques for
unsupervised machine learning tasks. First, a brief introduction to the main concepts related …

Minimizing the classification error rate and the number of selected features are the two major
objectives of feature selection, and they are often in conflict with each other, which is a …

The ubiquity and commoditization of wearable sensors have generated a deluge of user-
generated health care data and played a key role in clinical utility, particularly when …