With the pandemic subsiding, academic activities are reviving rapidly around the world. In May 2023, I organized the AI Forum 2023 and had the honor to invite Yaochu Jin, Kay Chen Tan, and Yew Soon Ong to deliver keynote speeches about cutting-edge CI technologies and, in particular, to share their valuable research experience at the panel discussion. One thing these former Editors-in-Chief of IEEE

Let's talk about the elephant in the room. ChatGPT and other Large Language Models (LLM) along with their AI cousins focused on images, graphics, math, coding, and audio are dominating our world of generating reports and manuscripts. They can be immensely fun: I asked ChatGPT to write me song lyrics about fuzzy logic in various genres. I wasn't crazy about the folk song, but I really liked ChatGPT's

On behalf of the organizing committee, we are delighted to deliver this conference report for the 2022 IEEE Symposium Series on Computational Intelligence (SSCI 2022), which was held in Singapore from 4th to 7th December 2022. IEEE SSCI is an established flagship annual international series of symposia on computational intelligence (CI) sponsored by the IEEE Computational Intelligence Society (CIS)

A Survey on Evolutionary Neural Architecture Search, by Y. Liu, Y. Sun, B. Xue, M. Zhang, G. G. Yen, and K. C. Tan, IEEE Transactions on Neural Networks and Learning Systems, Vol. 34, No. 2, Feb. 2023, pp. 550–570.

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing deep learning, especially deep reinforcement learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed neural combinatorial optimization (NCO). However, the advantages and disadvantages of NCO

For deep learning, size is power. Massive neural nets trained on broad data for a spectrum of tasks are at the forefront of artificial intelligence. These large pre-trained models or “Jacks of All Trades” (JATs), when fine-tuned for downstream tasks, are gaining importance in driving deep learning advancements. However, environments with tight resource constraints, changing objectives and intentions

The complex network has attracted increasing attention and shown effectiveness in modeling multifarious systems. Focusing on selecting members with good spreading ability, the influence maximization problem is of great significance in network-based information diffusion tasks. Plenty of attention has been paid to simulating the diffusion process and choosing influential seeds. However, errors and attacks

To apply convolutional neural networks (CNNs) on high-resolution images, a common approach is to split the input image into smaller patches. However, the field-of-view is restricted by the input size. To overcome the problem, a multi-magnification attention convolutional neural network (MMA-CNN) is proposed to analyze images based on both local and global features. Our approach focuses on identifying

Cooperative co-evolution (CC) is an evolutionary algorithm that adopts the divide-and-conquer strategy to solve large-scale optimization problems. It is difficult for CC to specify a suitable subpopulation size to solve different subproblems. The population diversity may be insufficient to search for the global optimum during subpopulations’ evolution. In this paper, an adaptive method for enhancing

Recently, multi-objective evolutionary algorithms (MOEAs) have shown promising performance in terms of community detection in complex networks. However, most studies have focused on designing different strategies to achieve good community detection performance based on a single population. Unlike these studies, this study proposes a macro-micro population-based co-evolutionary multi-objective algorithm

* 2023 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2023)

After attending numerous video conferences and missing IEEE WCCI 2022 in Padua due to the COVID-19 pandemic, I finally got to mingle with the IEEE SSCI 2022 and the A*STAR CFAR AI Symposium attendees in Singapore. Words could not express my sheer excitement for meeting with friends again “in person”. It was much more fun than staring at each other via individuals’ cold computer screens. And I must

As this issue is published, 2023 is well underway. I don't have a set theme for this message – I will just ramble along for a while writing about accomplishments, on-going activities, and plans for the Computational Intelligence Society. Not so original, eh?

Keeley Crockett is a Professor in Computational Intelligence at Manchester Metropolitan University. She gained a B.Sc. degree (hons) in computation from UMIST (1993), and a Ph.D. in Fuzzy Decision Trees from MMU (1998). Her research interests include the ethics of Artificial Intelligence (AI), fuzzy systems, psychological profiling using AI, fuzzy natural language processing, semantic similarity, conversational

Professor Yike Guo is the Provost of the Hong Kong University of Science and Technology (HKUST). He is concurrently a Chair Professor in the Department of Computer Science and Engineering.

In a previous communication [1], we highlighted the inaugural 2023 IEEE Conference on AI (IEEE CAI 2023) - https://cai.ieee.org/2023/ - our first conference with an industry focus. IEEE CAI 2023 is structured along six verticals, covering Industrial AI, AI in Healthcare/Life Science, Transportation/Aerospace, Energy, Earth System Decision Support, and Social Implications of AI/Privacy (Fig. 1). Here

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing, by D. Hong, L. Gao, J. Yao, N. Yokoya, J. Chanussot, U. Heiden, and B. Zhang, IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, No. 11, Nov. 2022, pp. 6518–6531.

Optimization and learning are two main paradigms of artificial intelligence in addressing complex real-world problems, with their respective focuses but frequently enhanced by each other. Evolutionary multi-objective optimization (EMO) algorithms are a family of nature-inspired algorithms widely used for solving multi-objective optimization problems (MOPs). Despite the great success achieved by the

Solving constrained multi-objective optimization problems (CMOPs) is one of the most popular research topics in the multi-objective optimization community. Various approaches based on different algorithmic strategies have been proposed for benchmark CMOPs with different features and challenges. Although most of these algorithms employ one or more fixed strategies, determining the most suitable strategy

Antimicrobial peptides (AMPs), which are parts of the innate immune response found among all classes of life, are promising in broad-spectrum antibiotics and drug-resistant infection treatments. Although AMPs effectively kill bacteria, numerous AMPs widely distributed in the sequence space remain unknown to humans. Therefore, the de novo design of AMPs involves the exploration of vast sequence space

In recent years, multilayer networks have served as effective models for addressing and analyzing real-world systems with multiple relationships. Among these scenarios, the community detection (CD) problem is one of the most prominent research hotspots. Although some research on multilayer network CD (MCD) has been proposed to address this problem, most studies focus only on topological structures

Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because it promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date, significant accomplishments have been reported in many areas that have been deemed challenging for machines, ranging

Neural architecture search (NAS) has been widely studied to design high-performance network architectures automatically. However, existing approaches require more search time and substantial resource consumption due to their intensive architecture evaluations. Moreover, recently developed NAS algorithms are noncompetitive when combining multiple competing and conflicting objectives, e.g., the test

This paper presents a solution to the video prediction problem based on the deep learning paradigm. Predicted frames generated by existing video prediction models are often blurry and have difficulty maintaining accuracy in multi-step prediction. To overcome these limitations, this paper presents a deep learning model, named the deep pixel restoration loop coding network (DPR-LC-Net), which employs

* 2023 IEEE Conference on Artificial Intelligence (IEEE CAI 2023)

Many state-of-the-art evolutionary algorithms (EAs) can be categorized as sequential hybrid EAs, in which various EAs are sequentially executed. The timing to switch from one EA to another is critical to the performance of the hybrid EA because the switching time determines the allocation of computational resources and thereby it helps balance exploration and exploitation. In this article, a framework

Δ 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2023)

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

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

It is the time when spring is dancing around the corner, and the ballet The Nutcracker has drawn a curtain on another season's stage. While reminiscing about the Sugar Plum Fairy's lissome steps gliding across the stage along with the light-hearted music notes, we might wonder how Pyotr Ilyich Tchaikovsky, together with choreographer Marius Petipa, first adapted and transformed the story into an artistic

Welcome to a new year in CIS! You are reading this in the first issue of CIM for 2023, but I'm writing this message right as the US switches from Daylight Savings Time to Standard Time. The title of this article is how I was taught to remember which direction to turn the clocks in this biannual ritual. But it now reminds me to reflect a little on 2022 and then to spring forward in anticipation of the

Donald C. Wunsch II (Fellow, IEEE) received the B.S. degree in applied mathematics from the University of New Mexico, Albuquerque, NM, USA, the Jesuit Honors Program from Seattle University, Seattle, WA, USA, the Kellogg Graduate Certificate of Nonprofit Management from Northwestern University, Evanston, IL, USA, the M.S. degree in applied mathematics from the University of Washington, Seattle, WA

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In a previous communication (Bonissone and Fogel, 2022), we highlighted the inaugural 2023 IEEE Conference on AI (IEEE CAI 2023) - https://cai.ieee.org/2023/ - our first conference with an industry focus. IEEE CAI 2023 is structured along six verticals, covering Industrial AI, AI in Healthcare/Life Science, Transportation/Aerospace, Energy, Earth System Decision Support, and Social Implications of

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The 2022 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (IEEE CIBCB 2022) was held from August 15th-17th in Ottawa, Canada. This conference has been held annually since 2004. After two years of virtual conferences, IEEE CIBCB 2022 was held primarily in person, with some remote participation. It was great to be back with friends and colleagues!

The IEEE Intelligence Society (CIS) offers many financially-sponsored conferences each year. All of these depend on the hard work and effort of volunteers who propose and then organize these events in the pursuit of a healthy exchange of information and improvement in the application and theory of computational intelligence approaches. The last few years in particular have been a challenging time for

“It has been widely recognized that the efficient training of neural networks (NNs) is crucial to classification performance. While a series of gradient-based approaches have been extensively developed, they are criticized for the ease of trapping into local optima and sensitivity to hyperparameters. Due to the high robustness and wide applicability, evolutionary algorithms (EAs) have been regarded

Computational Intelligence (CI) provides a set of powerful tools to effectively tackle complex computational tasks: global optimization methods (e.g., evolutionary computation, swarm intelligence), machine learning (e.g., neural networks), fuzzy reasoning, and so on. While CI research generally focuses on the improvement of algorithms (e.g., faster convergence, higher accuracy, reduced error), another

Several optimization problems have features that hinder the capabilities of searching heuristics. To cope with this issue, different methods have been proposed to manipulate search spaces and improve the optimization process. This paper focuses on Dilation Functions (DFs), which are one of the most promising techniques to manipulate the fitness landscape, by “expanding” or “compressing” specific regions

Graph learning is a popular approach for perfor ming machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure

Influence maximization is a crucial issue for mining the deep information of social networks, which aims to select a seed set from the network to maximize the number of influenced nodes. To evaluate the influence spread of a seed set efficiently, existing studies have proposed transformations with lower computational costs to replace the expensive Monte Carlo simulation process. These alternate transformations

This paper proposes a self-supervised framework based on a contrastive auto-encoding and convolutional information exchange for multi-modal medical fusion tasks. It is well known that multi-modal medical images have the same and unique features, and information redundancy is easily led when source image features are extracted in pairs. Inspired by contrastive learning, this article constructs positive

Fuzzy sets are suitable for modeling human linguistic uncertainties, and the resulting fuzzy systems can then be used to perform inferences based on a linguistic rulebase. They have been successfully used in numerous applications, particularly controls. This short paper introduces the basics of type-1 and interval type-2 fuzzy sets and systems, particularly for newcomers. The online full paper at IEEE

Presents the 2022 author/subject index for this issue of the publication.

Electroencephalogram (EEG) based brain-computer interfaces (BCIs) are used in many applications, due to their low-risk, low-cost, and convenience. Because of EEG’s high variations across subjects and sessions, a long calibration session is usually needed to adjust the system before each use, which is time-consuming and user-unfriendly. Though various machine learning approaches have been proposed to

Establishing cognitive environments help cognitive robots understand human actions, languages and observed objects. In this paper, a cognitive robotic model based on a novel spiking bidirectional associative memory (BAM) method is presented to establish a cognitive environment. The spiking BAM network uses a supervised spike-based learning rule to learn the relationship between the semantic information

This tutorial paper surveys provably optimal alternatives to end-to-end backpropagation (E2EBP) — the de facto standard for training deep architectures. Modular training refers to strictly local training without both the forward and the backward pass, i.e., dividing a deep architecture into several nonoverlapping modules and training them separately without any end-to-end operation. Between the fully

This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides

As an important research topic of machine learning, multiclass classification has wide applications ranging from computer vision to bioinformatics. A variety of multiclass classification algorithms with promising performance have been proposed. Among them, the decomposition-based algorithms have shown their competitiveness, since they transform the original problem into several easily solved binary

Presents the CIS society calendar of upcoming events and meetings.

Presents the front cover 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.

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

The employment of machine learning often requires a large amount of data, including our sensitive and personal information, to train models for various applications. With growing worldwide concerns over data privacy, particularly with the recent introduction of the EU’s General Data Protection Regulation (GDPR), it has become an important topic for AI research and application to implement data privacy-preserving

Hi all, To borrow from The Bard, what’s in a name? The world is awash with the hype, the reality, the potential, and the concerns of Artificial Intelligence. But what constitutes AI? That depends on who, and when, you ask. When I was a kid (academically speaking), tasked with teaching a course on AI, I “knew” what it was: systems with propositions consisting of discrete symbols, manipulated through

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

Welcome to a new column of the IEEE Computational Intelligence Magazine devoted to Industrial Activities. In a previous communication1, I described the vision for the newly formed IEEE CIS Industrial and Governmental Activities Committee (IGAC). With IGAC, we strive to provide services, products, and offerings of interest to Industry, ranging from webinars to tutorials, conferences, publications, standards