Presents the front cover for this issue of the publication.

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Presents the table of contents for this issue of this publication.

Provides a listing of current staff, committee members and society officers.

The focus on evolutionary neural architecture search in this issue has driven me to ponder the evolution of biological neural networks, or rather, the human brain. Researchers in neuroanatomy found that the cognitive and mental development of humans is attributed to the increase of our brain size throughout evolution. Yet bigger is not always better. It was also discovered that, once the brain reaches

Ethics in Artificial Intelligence (AI) is discussed everywhere, in governmental circles as well as scientific forums. It is very often associated with the two widely used concepts of diversity and eXplainable Artificial Intelligence (XAI). The latter was promoted by DARPA, and it is expected to propose methods preserving the rights of users to understand how the AI systems work and why decisions are

Provides a listing of current committee members and society officers.

Republishes 7 book reviews first appearing in various IEEE journals.

Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.

This special issue has brought together researchers to report state-of-the-art contributions on the latest research and development, up-to-date issues, challenges, and applications in the field of ENAS. Following a rigorous peer review process, five papers have been accepted for publication in this special issue. The articles included are briefly summarized.

While deep neural networks (DNNs) deliver state-of-the-art accuracy on various applications from face recognition to language translation, it comes at the cost of high computational and space complexity, hindering their deployment on edge devices. To enable efficient processing of DNNs in inference, a novel approach, called Evolutionary Multi-Objective Model Compression (EMOMC), is proposed to optimize

Unsupervised visual representation learning is one of the hottest topics in computer vision, yet performance still lags behind compared with the best supervised learning methods. At the same time, neural architecture search (NAS) has produced state-of-the-art results on various visual tasks. It is a natural idea to explore NAS as a way to improve unsupervised representation learning, yet it remains

Recently proposed neural architecture search (NAS) algorithms adopt neural predictors to accelerate architecture search. The capability of neural predictors to accurately predict the performance metrics of the neural architecture is critical to NAS, but obtaining training datasets for neural predictors is often time-consuming. How to obtain a neural predictor with high prediction accuracy using a small

Wind energy is considered one of the fastest growing renewable ('green') energy resources. Precise wind power forecasting is imperative to ensure reliable power system planning and wind farm operation. However, traditional methods cannot yield satisfactory forecasts because of the chaotic properties and high volatility of wind speed time series. To address this issue, the use of artificial neural networks

Recently, convolutional neural networks (CNNs) have achieved great success in the field of artificial intelligence, including speech recognition, image recognition, and natural language processing. CNN architecture plays a key role in CNNs' performance. Most previous CNN architectures are hand-crafted, which requires designers to have rich expert domain knowledge. The trial-and-error process consumes

Rolling bearing fault diagnosis is an important task in mechanical engineering. Existing methods have several limitations, such as requiring domain knowledge and a large number of training samples. To address these limitations, this paper proposes a new diagnosis approach, i.e., multiview feature construction based on genetic programming with the idea of ensemble learning (MFCGPE), to automatically

Provides a listing of upcoming conference events of interest to practitioners and researchers.

Advertisement, IEEE.

Assessment of individuals' job performance, personalized health and psychometric measures are domains where data-driven ubiquitous computing will have a profound impact in the near future. Existing work in these domains focus on techniques that use data extracted from questionnaires, sensors (wearable, computer, etc.), or other traits to assess well-being and cognitive attributes of individuals. However

Advertisement, IEEE.

Recent advances in deep learning have had a methodological and practical impact on brain-computer interface (BCI) research. Among the various deep network architectures, convolutional neural networks (CNNs) have been well suited for spatio-spectral-temporal electroencephalogram (EEG) signal representation learning. Most of the existing CNN-based methods described in the literature extract features

Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical

Presents the front cover for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the table of contents for this issue of this publication.

Provides a listing of current staff, committee members and society officers.

Presents the introductory editorial for this issue of the publication.

Presents the President’s message for this issue of the publication.

Provides a listing of current committee members and society officers.

Provides a listing of current committee members and society officers.

Lists society members elevated to IEEE Fellow grade for 2021. Brief biographies are included.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents summaries of recent books published in the field of computational intelligence.

This paper introduces neuroevolution for solving differential equations. The solution is obtained through optimizing a deep neural network whose loss function is defined by the residual terms from the differential equations. Recent studies have focused on learning such physics-informed neural networks through stochastic gradient descent (SGD) variants, yet they face the difficulty of obtaining an accurate

This paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Each player's payoff is influenced by the amount evaded and the subjective probability of being inspected by tax authorities. Since transactions between companies

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Planning driving routes is a common yet challenging task. Previous solutions that often suggest the shortest or time-dependent fastest paths for drivers are incompatible with the diversity of user requirements. Recent years are witnessing an increasing need of utility gained along the driving path and more advanced pathplanners are developed correspondingly, such as the safest and the most beautiful

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Provides a notice of upcoming conference events of interest to practitioners and researchers.

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Describes the above-named upcoming conference event. May include topics to be covered or calls for papers.

Digital Object Identifier: 10.1109/ TNNLS.2019.2945133 “The aim of multi-output learning is to simultaneously predict multiple outputs given an input. It is an important learning problem for decision-making since making decisions in the real world often involves multiple complex factors and criteria. In recent times, an increasing number of research studies have focused on ways to predict multiple

T he capacitated arc routing problem is an important NP-hard problem with numerous realworld applications. The capacitated arc routing problem with uncertainties refers to those instances where there are uncertainties in decision variables, objective functions and/or constraints. The capacitated arc routing problem with uncertainties captures real-world situations much better than a static capacitated

In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to

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

Minimax optimization is a widely-used formulation for robust design in multiple operating or environmental scenarios, where the worst-case performance among multiple scenarios is the optimization objective requiring a large number of quality assessments. Consequently, minimax optimization using evolutionary algorithms becomes prohibitive when each quality assessment involves computationally expensive

Due to the availability of huge amounts of data and processing abilities, current artificial intelligence (AI) systems are effective in solving complex tasks. However, despite the success of AI in different areas, the problem of designing AI systems that can truly mimic human cognitive capabilities such as artificial general intelligence, remains largely open. Consequently, many emerging cross-device

Cost-sensitive learning is a popular approach to addressing the problem of class imbalance for many classification algorithms in machine learning. However, most cost-sensitive algorithms are dependent on manually designed cost matrices. Unfortunately, in many cases, it is often not easy for humans, even experts, to accurately specify misclassification costs for different mistakes due to the lack of

Presents the front cover for this issue of the publication.

Presents information on the IEEE CEC 2021 Conference.

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

Presents the introductory editorial for this issue of the publication.

Presents the President’s message for this issue of the publication.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Presents the vision statement by the IEEE CIS VP of publications.

Presents the vision statement of the IEEE CIS VP of member activities.

Presents information on the 2020 IEEE World Congress on Computational Intelligence.

Presents the CIS society calendar of upcoming events and meetings.