Abstract

Abstract

Abstract
This article serves as an introduction to the Special Issue on Metalearning and Algorithm Selection. The introduction is divided into two parts. In the the first section, we give an overview of how the field of metalearning has evolved in the last 1-2 decades and mention how some of the papers in this special issue fit in. In the second section, we discuss the contents of this special issue. We divide the papers into thematic subgroups, provide information about each subgroup, as well as about the individual papers. Our main aim is to highlight how the papers selected for this special issue contribute to the field of metalearning.

Abstract
This paper describes the collaborative learning environment, aligned with the United Nations Millennium Development Goals, provided by the European Project Semester (EPS). EPS is a one semester capstone project programme offered by eighteen European engineering schools as part of their student exchange programme portfolio. In this international programme, students are organized in teams, grouping individuals from diverse academic backgrounds and nationalities. The teams, after choosing a project proposal, become fully responsible for the conduction of their projects. By default, project proposals refer to open multidisciplinary real problems. The purpose of the project is to expose students to problems of a greater dimension and complexity than those faced throughout the degree programme as well as to put them in contact with the so-called real world, in opposition to the academic world. EPS provides an integrated framework for undertaking capstone projects, which is focused on multicultural and multidisciplinary teamwork, communication, problem-solving, creativity, leadership, entrepreneurship, ethical reasoning and global contextual analysis. Specifically, the design and development of sustainable systems for growing food allow students not only to reach the described objectives, but to foster sustainable development practices. As a result, we recommend the adoption of this category of projects within EPS for the benefit of engineering students and of the society as a whole.

Abstract
Human gait analysis is of utmost importance in understanding several aspects of human movement. In clinical practice, characterizing movement in order to obtain accurate and reliable information is a major challenge, and physicians usually rely on direct observation in order to evaluate a patient's motor abilities. In this contribution, a system that can objectively analyze the patients gait and generate an on the fly, targeted and optimized gait analysis report is presented. It is an extension to an existing system that could be used without interfering with the healthcare environment, which did not provide any on the fly feedback to physicians. Patient data are acquired using Kinect v2, followed by data processing, gait specific feature extraction, ending with the generation of a quantitative on the fly report. To the best of our knowledge, the complete system fills the gap as a proper gait analysis system, i.e., a low-cost tool that can be applied without interfering with the healthcare environment, provide quantitative gait information and on the fly feedback to physicians through a motion quantification report that can be useful in multiple areas. © 2018 IEEE.

Abstract
Bioinspired search algorithms are widely used for solving optimization problems. The evolution progress isusially measured by the fitness value of the population fittest element. The search stops when the algorithm reaches a predetermined number of iterations, or when no improvement is achieved after some iterations. Usually, no information, behind the best global objective value, is fed into the algorithm to influence its behavior. In this paper, a entropy metric is proposed to measure the algorithm convergence. Several experiments are carried out using a particle swarm optimization to analyze the metric relevance. Moreover, the proposed metric is used to implement a strategy to prevent premature convergence to suboptimal solutions. The results show that the index is useful for analyzing and improving the algorithm convergence during the evolution.