The IEEE Computational Intelligence Society annually recognizes significant contributions and meritorious service in the field of computational intelligence.

Nominations are highly encouraged to recognize our volunteers and eminent colleagues to keep our Society alive and to promote research excellence in computational intelligence. The IEEE Computational Intelligence Society sponsors several annual Awards. Please see the awards and nomination details on the IEEE CIS Awards page.

The IEEE CIS Awards have a deadline of 30 April 2023 (strict deadline). 

The IEEE Computational Intelligence Society sponsors many conferences each year. We are currently soliciting proposals for the following upcoming conferences:

• 2025 IEEE Congress on Evolutionary Computation (IEEE CEC 2025)
• 2025 FUZZ-IEEE Conference (FUZZ-IEEE 2025)
• 2026 IEEE World Congress on Computational Intelligence (WCCI 2026)

IEEE CEC 2025 and FUZZ-IEEE 2025 are the society’s flagship conferences for evolutionary computation and fuzzy systems respectively. WCCI 2026 combines these conferences with the International Joint Conference on Neural Networks, our flagship conference on neural networks.

CEC 2023 will be held in the USA. FUZZ-IEEE 2023 will be held in Korea. IEEE WCCI 2024 will be held in Japan. We are hopeful that some bids will be submitted from other geographical areas.

Proposals for the IEEE CEC 2025 and FUZZ-IEEE 2025 conferences should be submitted by 30 April 2023. Proposals for IEEE WCCI 2026 conference should be submitted by 30 June 2023 (hard deadlines). This will provide sufficient time for review and decision by IEEE CIS Conferences Committee and Administrative Committee.

Please inform VP Conferences, Marley Vellasco ([email protected]) and Chair of the Confcom Subcommittee on Future Conferences, Steven Corns ([email protected])of your intention to provide a proposal or if you would like further information. The process for generating such a proposal can be found on our website.

Distinguished Lecturers present novel basic and/or applied research results in their CIS sub-field. Their lectures offer insights into the trends and challenges of their CIS sub-field and their vision for the given sub-field. This year we have 19 lecturers in various CIS sub-fields. 

The program supports local CIS Chapters by enabling a major Chapter event which can considerably improve a Chapter’s visibility to their existing IEEE CIS members, other IEEE Society members and the wider CIS community in their area. For more details on the  process of organizing distinguished lectures please click here.

It should be noted that this program is NOT intended as a means to finance speakers for conferences/workshops or symposia.

Please visit the Distinguished Lectureres Program for more information. 

What is your title, and place of work? ( or Technical Field of Research)?

Professor at the Department of Computer Science, The Norwegian University of Science and Technology (NTNU), Trondheim, Norway

How long have you been a member of CIS and what was the reason you chose to join IEEE CIS?

25+ years. Originally, I joined IEEE for the benefit of reduced Conference Fees and then CIS when I took on the role of Program Chair for a CIS conference.

What Computational Intelligence society committee do you serve?

My main CIS roles are currently in the Conference Committee (ConfCom), the Strategic Planning Committee (SPC), and the Administrative Committee (ADCOM).

What have you learned from your experience and how has it helped you professionally?

Within CIS, I have been given the possibility to learn more about our broader CI field and the society itself though collaboration with many knowledgeable colleagues. However, I have also been given a voice and respect that my opinion matters, whether someone agrees or not. Working in such an environment has boasted my skills in when to listen, when to speak and how to present your input, even critic, in a respectful way.

At times my nomination to certain leadership roles has come as a big surprise. However, my international colleagues seem to know me better than I know myself and the variety of roles I have taken on have allowed me to grow into these roles and develop new skills that can be applied to any leadership role.

What has been the most fun/rewarding thing about being a volunteer for the IEEE Computational Intelligence Society? What have you enjoyed the most?

In brief, being part of the CIS family. I enjoy being part of the different committees and working as a team to address today’s and tomorrow’s challenges, reaching out way beyond my own research niche to contribute to the broader CI field.

The secret to success within CIS is simple too. You need to work hard on the tasks you are given, contribute to discussions and be respectful to your colleagues’ opinions. It is so simple and easy to succeed and a lot of fun in the process.

Tell us something about you that we don't know.

Although living in Trondheim, Norway, our second home is in the mountains. A cabin in a beautiful location with running water from a stream, hidden under the snow and ice in the winter months. You’ll find me out in the mountains skiing, hiking, picking berries or mushrooms, depending on the season.

Type-1 and Interval Type-2 Fuzzy Systems [AI- eXplained]

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 Xplore provides detailed examples for better understanding, and interactive components for a hands-on exploration. Read More

IEEE Computational Intelligence Magazine, February 2023

A Survey on Evolutionary Neural Architecture Search

Deep neural networks (DNNs) have achieved great success in many applications. The architectures of DNNs play a crucial role in their performance, which is usually manually designed with rich expertise. However, such a design process is labor-intensive because of the trial-and-error process and also not easy to realize due to the rare expertise in practice. Neural architecture search (NAS) is a type of technology that can design the architectures automatically. Among different methods to realize NAS, the evolutionary computation (EC) methods have recently gained much attention and success. Unfortunately, there has not yet been a comprehensive summary of the EC-based NAS algorithms. This article reviews over 200 articles of most recent EC-based NAS methods in light of the core components, to systematically discuss their design principles and justifications on the design. Furthermore, current challenges and issues are also discussed to identify future research in this emerging field. Read More

IEEE Transactions on Neural Networks and Learning Systems, February 2023

Fuzzy Deep Forest With Deep Contours Feature for Leaf Cultivar Classification

Deep learning is a compelling technique for feature extraction due to its adaptive capacity of processing and providing deeper image information. However, for the task of leaf cultivar classification, the deep learning-based classifier model is unable to extract contour features of leaf images deeply due to the lack of large specialized datasets and expert knowledge annotations. Also, the scale/size of the current leaf cultivar dataset does not meet the needs of deep neural networks (DNNs). In particular, the high model complexity of DNNs implies that deep-learning-based neural networks seem to must require a large dataset to achieve good performance, but facing the fact that the leaf cultivar dataset often is small, even some classes in this kind of datasets contain less than ten images/examples. To overcome these problems and inspired by the resounding success of fuzzy logic, we propose a novel fuzzy ensemble model for leaf cultivar classification. Read more

IEEE Transactions on Fuzzy Systems, December 2022

A New Perspective on Stabilizing GANs Training: Direct Adversarial Training

Generative Adversarial Networks (GANs) are the most popular image generation models that have achieved remarkable progress on various computer vision tasks. However, training instability is still one of the open problems for all GAN-based algorithms. Quite a number of methods have been proposed to stabilize the training of GANs, the focuses of which were respectively put on the loss functions, regularization and normalization technologies, training algorithms, and model architectures. Different from the above methods, in this paper, a new perspective on stabilizing GANs training is presented. It is found that sometimes the images produced by the generator act like adversarial examples of the discriminator during the training process, which may be part of the reason causing the unstable training of GANs. With this finding, we propose the Direct Adversarial Training (DAT) method to stabilize the training process of GANs. Read More

IEEE Transactions on Emerging Topics in Computational Intelligence, February 2023

Hierarchy Ranking Method for Multimodal Multiobjective Optimization With Local Pareto Fronts

We present concepts and recipes for the anytime performance assessment when bMultimodal multiobjective problems (MMOPs) commonly arise in real-world situations where distant solutions in decision space share a very similar objective value. Traditional multimodal multiobjective evolutionary algorithms (MMEAs) prefer to pursue multiple Pareto solutions that have the same objective values. However, a more practical situation in engineering problems is that one solution is slightly worse than another in terms of objective values, while the solutions are far away in the decision space. In other words, such problems have global and local Pareto fronts (PFs). In this study, we proposed several benchmark problems with several local PFs. Then, we proposed an evolutionary algorithm with a hierarchy ranking method (HREA) to find both the global and the local PFs based on the decision maker’s preference. Regarding HREA, we proposed a local convergence quality evaluation method to better maintain diversity in the decision space. Moreover, a hierarchy ranking method was introduced to update the convergence archive. The experimental results show that HREA is competitive compared with other state-of-the-art MMEAs for solving the chosen benchmark problems. Read More

IEEE Transactions on Evolutionary Computation, February 2023

Conversational Affective Social Robots for Ageing and Dementia Support

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation, and patient benefit remain immature. Affective human–robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and human–robot engagement. In this article, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centered approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarize the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Read More

IEEE Transactions on Cognitive and Developmental Systems, December 2022

Measuring Control to Dynamically Induce Flow in Tetris

Dynamic difficulty adjustment (DDA) is a set of techniques that aim to automatically adapt the difficulty of a video game based on the player’s performance. This article presents a methodology for DDA using ideas from the theory of flow and case-based reasoning (CBR). In essence, we are looking to generate game sessions with a similar difficulty evolution to previous game sessions that have produced flow in players with a similar skill level. We propose a CBR approach to dynamically assess the player’s skill level and adapt the difficulty of the game based on the relative complexity of the last game states. We develop a DDA system for Tetris using this methodology and show, in an experiment with 40 participants, that the DDA version has a measurable impact on the perceived flow using validated questionnaires. Read More

IEEE Transactions on Games, December 2022

Call for Papers - Artificial Intelligence eXplained (AI-X) - (Immersive Article)

After the introduction of immersive articles to the IEEE Computational Intelligence Magazine, we ask authors to explore this new paper format and prepare a submission for the special issue "Artificial Intelligence eXplained." The special issue is focusing on the integration of interactive components and how they can be used to explain AI concepts or methods. Please view the call for papers here.

To get started, you can check out a recent webinar that highlighted the interesting opportunities that immersive articles can provide to our community. The recording of the webinar is openly available on The IEEE CIS YouTube channel and goes through the details of the immersive article template.

• IEEE TEVC Special Issue on Evolutionary Neural Architecture Search (28 March 2023 )

• IEEE TCDS Special Issue on "Advancing Machine Intelligence with Neuromorphic Computing (31 March 2023)

• IEEE TNNLS Special Issue on Learning Theories and Methods with Application to Digitized Process Manufacturing (31 March 2023)

• IEEE TNNLS Special Issue on Deep Learning: When and How? (23 April 2023 )

• IEEE TNNLS Special issue on Trustworthy Federated Learning (1 June 2023)

• IEEE TAI Special Issue on AutoML for Non-Stationary Data (2 June 2023)

• IEEE TFS Special Issue on Smart Fuzzy Optimization in Sustainable Manufacturing and Supply Chain Management (1 November 2023)

• IEEE TFS Special Issue on Fuzzy-Deep Neural Network Learning in Sentiment Analysis (15 January 2024)

  
  

2023 IEEE Conference on Artifical Intelligence (2023 IEEE CAI) Call for Participation

The IEEE Conference on Artificial Intelligence (IEEE CAI) will take place in Santa Clara, CA, USA on 5-6 June 2023. This new conference series emphasizes AI applications in specific verticals that impact industrial technology and innovation. Key verticals include Energy, Healthcare and Life Science, Transportation and Aerospace, Earth Systems Decision Support, Industrial AI, and Ethical and Societal Implications of AI. IEEE CAI seeks original, high-quality poster papers describing the research and results that contribute to advancements in AI applications.

IEEE 2023 Congress on Evolutionary Computation

IEEE CEC 2023 is a world-class conference that brings together researchers and practitioners in the field of evolutionary computation and computational intelligence from around the globe. The conference program includes plenary lectures, regular and special sessions, tutorials, competitions, workshops and panel discussions. IEEE CEC 2023 is calling for Call for Late Breaking Papers and Journal to Conference (J2C) Papers.