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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Presents an interview conducted with the founding Editor-in-Chief of IEEE Transactions on Artificial Intelligence, Prof. Hussein Abbass.

Reports on the IEEE DSAA—Transdisciplinary Data Science initiative led by the IEEE The IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA). This conference aims to be a premier forum for addressing the above ever increasing and important demand, data volume and complexities, and the associated business problems, opportunities, decisions and values in a translational and

Presents new books of interest to CIS society members.

Dataset shifts are present in many real-world applications, since data generation is not always fully controlled and is subject to noise, degradation, and other natural variations. In machine learning, the lack of regularity in data can degrade performance by breaching error constraints. Different methods have been proposed to solve shifting problems; however, shifts in off-line learning mode are not

The success of any machine learning technique depends on the correct setting of its parameters and, when it comes to large-scale datasets, hand-tuning these parameters becomes impractical. However, very large-datasets can be pre-processed in order to distil information that could help in appropriately setting various systems parameters. In turn, this makes sophisticated machine learning methods easier

Depression is considered a serious medical condition and a large number of people around the world are suffering from it. Within this context, a lot of studies have been proposed to estimate the degree of depression based on different features and modalities, specific to depression. Supported by medical studies that show how depression is a disorder of impaired emotion regulation, we propose a different

This paper proposes an approach to strength adjustment and assessment for Monte-Carlo tree search based game-playing programs. We modify an existing softmax policy with a strength index to choose moves. The most important modification is a mechanism which filters low-quality moves by excluding those that have a lower simulation count than a pre-defined threshold ratio of the maximum simulation count

Presents the CIS society calendar of upcoming events and meetings.

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

Presents information on the IEEE SSCI 2020 Conference.

Presents the front cover for this issue of the publication.

Presents information on hte IEEE SSCI 2020 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 the introductory editorial 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 President’s message for this issue of the publication.

Presents a listing of the newly elected CIS Administrative Committee members.

Presents a listing of AESS members who were elevated to the status of IEEE Fellow.

Presents new books of interest to CIS society members.

This paper defines a new combinatorial optimization problem, namely General Combinatorial Optimization Problem (GCOP), whose decision variables are a set of parametric algorithmic components, i.e. algorithm design decisions. The solutions of GCOP, i.e. compositions of algorithmic components, thus represent different generic search algorithms. The objective of GCOP is to find the optimal algorithmic

The aim of network structural balance is to find proper partitions of nodes that guarantee equilibrium in the system, which has attracted considerable attention in recent decades. Most of existing studies focus on reducing imbalanced components in complex networks without considering the tolerance of these balanced networks against attacks and failures. However, as indicated by some recent studies

To represent a many-objective Pareto-optimal front having four or more dimensions of the objective space, a large number of points are necessary. However, for choosing a single preferred point from a large set is problematic and time-consuming, as they provide a large cognitive burden on the part of the decision-makers (DMs). Hence, many-objective optimization and decision-making researchers and practitioners

Recent years have witnessed remarkable successes of machine learning in various applications. However, machine learning models suffer from a potential risk of leaking private information contained in training data, which have attracted increasing research attention. As one of the mainstream privacy- preserving techniques, differential privacy provides a promising way to prevent the leaking of individual-level

Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation

Owing to the continual growth of multimodal data (or feature spaces), we have seen a rising interest in multimedia applications (e.g., object classification and searching) over these heterogeneous data. However, the accuracy of classification and searching tasks is highly dependent on the distance estimation between data samples, and simple Euclidean (EU) distance has been proven to be inadequate.

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

Presents the CIS calendar of upcoming events and meetings.

Presents information on the 2020 IEEE Cog Conference.

Presents information on the IEEE WCCI 2020 conference.

Wireless big data contain valuable information on users' behaviors and preferences, which can drive the design and optimization for wireless systems. The fundamental issue is how to mine mobile intelligence and further incorporate them into wireless systems. To this end, this article discusses two challenges on big data based wireless system design and optimization, and proposes a unified framework

With the exponential expansion of the number of Internet of Things (IoT) devices, many state-of-the-art communication technologies are being developed to use the lowerpower but extensively deployed devices. Due to the limits of pure channel characteristics, most protocols cannot allow an IoT network to be simultaneously large-scale and energy-efficient, especially in hybrid architectures. However,

Ant colony optimization is a swarm intelligence metaheuristic inspired by the foraging behavior of some ant species. Ant colony optimization has been successfully applied to challenging optimization problems. This article investigates existing ant colony optimization algorithms specifically designed for combinatorial optimization problems with a dynamic environment. The investigated algorithms are

The explosive growth of wireless devices motivates the development of the internet-of-things (IoT), which is capable of interconnecting massive and diverse "things" via wireless communications. This is also called massive machine type communications (mMTC) as a part of the undergoing fifth generation (5G) mobile networks. It is envisioned that more sophisticated devices would be connected to form a

Fuzzy Sets and Systems is an area of computational intelligence, pioneered by Lotfi Zadeh over 50 years ago in a seminal paper in Information and Control. Fuzzy Sets (FSs) deal with uncertainty in our knowledge of a particular situation. Research and applications in FSs have grown steadily over 50 years. More recently, we have seen a growth in Type-2 Fuzzy Set (T2 FS) related papers, where T2 FSs are

Michigan-style learning classifier systems (M-LCSs) represent an adaptive and powerful class of evolutionary algorithms which distribute the learned solution over a sizable population of rules. However their application to complex real world data mining problems, such as genetic association studies, has been limited. Traditional knowledge discovery strategies for M-LCS rule populations involve sorting

Presents the front cover for this issue of the publication.

Presents information on the IEEE WCCI 2020 Glasgow 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 the introductory editorial for this issue of the publication.

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

Presents a listing of CIS society officers abd board members.

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

Reports on the activities of the Women in Computational Intelligence Sub-Committee (WCI) which develops, promotes, organizes and runs activities directed to achieve and ensure equal opportunities to both genders in the society’s life and computational intelligence arena.

Presents a listing of recent books in the computational intelligence industry.

This special issue focuses on the research challenges of computational intelligence technologies toward trustworthy IoT in the Big Data Era.

When humans learn several skills to solve multiple tasks, they exhibit an extraordinary capacity to transfer knowledge between them. The authors present here the last enhanced version of a bioinspired reinforcement-learning (RL) modular architecture able to perform skill-to-skill knowledge transfer and called transfer expert RL (TERL) model. TERL architecture is based on a RL actor-critic model where

With the increasing complexity of network topologies and architectures, adding intelligence to the network control plane through Artificial Intelligence and Machine Learning (AI&ML) is becoming a trend in network development. For large-scale geo-distributed systems, determining how to appropriately introduce intelligence in networking is the key to high-efficiency operation. In this treatise, we explore

Internet of Energy (IoE), the paradigm of applying Internet of Things (IoT) to energy management systems, aims to improve energy systems' efficiency and reliability by enhancing connectivity and interoperability among geographically distributed energy devices. This requires distributed computational intelligence responsible for data processing and decision making of the energy devices. To achieve distributed

The ever-evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction and affective computing. Traditional sensor-based and vision-based user behavior analysis approaches are obtrusive in general, hindering their usage in real-world. Therefore

Over the past two decades, Evolutionary Multi-objective Optimization (EMO) algorithms have demonstrated their ability to find and maintain multiple trade-off solutions in two and three-objective problems, making EMO as one of the most emergent and exciting fields of research and application within Computational Intelligence (CI) area. The main reason for EMO's success is that the population-based EMO

Machine Intelligence (MI) technologies have revolutionized the design and applications of computational intelligence systems, by introducing remarkable scientific and technological enhancements across domains. MI can improve Internet of Things (IoT) in several ways, such as optimizing the management of large volumes of data or improving automation and transmission in large-scale IoT deployments. When