Featured articles in IEEE CIS Transactions/Magazine are selected by the Editor-in-Chief to showcase a diverse and balanced set of topics from their individual Transactions/Magazine. Due to the promotion context of featured articles, the selection is left at the discretion of the Editor-in-Chief to identify articles that he/she sees relevant to the promotion.
Evolutionary Transfer Optimization - A New Frontier in Evolutionary Computation Research
Kay Chen Tan, Liang Feng, and Min Jiang
IEEE Computational Intelligence Magazine (Volume: 16, Issue: 1, Feb. 2021)
Abstract: The evolutionary algorithm (EA) is a nature-inspired population-based search method that works on Darwinian principles of natural selection. Due to its strong search capability and simplicity of implementation, EA has been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional exact mathematical approaches, such as linear programming, quadratic programming, and convex optimization. Despite its great success, it is worth noting that traditional EA solvers start the search from scratch by assuming zero prior knowledge about the task at hand. However, as problems seldom exist in isolation, solving one problem may yield useful information for solving other related problems. There has been growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance. This paper provides an overview of existing works of ETO based on the type of problems being solved by these methods, which are ETO for Optimization in Uncertain Environment, ETO for Multitask Optimization, ETO for Complex Optimization, ETO for Multi/Many-Objective Optimization, and ETO for Machine Learning Applications. The paper also highlights some of the challenges faced in this emerging research field of computational intelligence and discusses some promising future research directions in ETO. It is hoped that the study presented in this paper can help to inspire the development of more advanced ETO methods and applications.
Index Terms: Deep learning, Evolutionary computation, Search problems, Linear programming, Quadratic programming, Task analysis, Optimization
IEEE Xplore Link: https://ieeexplore.ieee.org/document/9321762
Computational Intelligence Techniques for Combating COVID-19: A Survey
Vincent S. Tseng, Josh Jia-Ching Ying, Stephen T.C. Wong, Diane J. Cook, and Jiming Liu
IEEE Computational Intelligence Magazine (Volume: 15, Issue: 4, Nov. 2020)
Abstract: Computational intelligence has been used in many applications in the fields of health sciences and epidemiology. In particular, owing to the sudden and massive spread of COVID-19, many researchers around the globe have devoted intensive efforts into the development of computational intelligence methods and systems for combating the pandemic. Although there have been more than 200,000 scholarly articles on COVID-19, SARS-CoV-2, and other related coronaviruses, these articles did not specifically address in-depth the key issues for applying computational intelligence to combat COVID-19. Hence, it would be exhausting to filter and summarize those studies conducted in the field of computational intelligence from such a large number of articles. Such inconvenience has hindered the development of effective computational intelligence technologies for fighting COVID-19. To fill this gap, this survey focuses on categorizing and reviewing the current progress of computational intelligence for fighting this serious disease. In this survey, we aim to assemble and summarize the latest developments and insights in transforming computational intelligence approaches, such as machine learning, evolutionary computation, soft computing, and big data analytics, into practical applications for fighting COVID-19. We also explore some potential research issues on computational intelligence for defeating the pandemic.
Index Terms: Diseases, COVID-19, Pandemics, Viruses (medical), Data analysis, Computational intelligence, Coronaviruses, Research initiatives
IEEE Xplore Link: https://ieeexplore.ieee.org/document/9225219