Welcome from the CIS President

Thankfully, 2022 marked the return for CIS to in-person meetings with our two major conferences, WCCI in Padua, Italy, and SSCI in Singapore. We owe a big round of thanks to the Chairs and Organizing Committees of those two events in tackling the uncertainty of in person meetings as the planet moved to the new reality of dealing with the lingering effects of the pandemic. Both conferences were very successful – check out the Proceedings on IEEE Xplore. We held the CIS AdCom meeting series prior to both events. It felt so good (if a little strange at first) to conduct our affairs really across the table from one another. The end of 2022 also marked the first example of reader immersive content in IEEE Xplore. This feature, debuted in the November 2022 issue of the IEEE Computational Intelligence Magazine resulted from a close collaboration between CIS and the IEEE staff teams across multiple OUs. It is really cool, so please check it out in Xplore. Looking to expand this capability in the future.

On to looking forward to 2023. Big things are planned. I invite you to spend some time on our website perusing the many benefits and opportunities of being an IEEE CIS member. Each year we look to increase the funding and engagement chances in members activities, technical activities, educational activities and more. Please check out the excellent newsletter for month-to-month announcements (archived versions are on the CIS website). In addition to these regular (and expanding) programs, IEEE allows us each year to invest in new initiatives to benefit our members and society. Many of our regular programs actually started as initiatives. This year, we have initiatives that span our entire society. They include, among others, enhancing the Resource Center; producing AI tools to help our publications efforts; obtaining better conference management software; industry and government outreach; post pandemic technical regeneration/revitalization – called "breaking boundaries”; developing qualitative metrics to measure our diversity and inclusion efforts, and more. Be on the lookout for developments in these areas.

As always, feel free to contact me at [email protected] with your thoughts, suggestions, questions, and innovative ideas (maybe you can "champion" a CIS initiative). Please stay safe and healthy. I hope to see many of you as we emerge from the pandemic.

Congratulations to Marios Polycarpou

Recipient of the 2023 IEEE Frank Rosenblatt Technical Field Award

Polycarpou pioneered the development of neural network-based fault monitoring and event detection. Its wide use in today’s monitoring and control systems makes reliable operation in diverse applications possible

The IEEE Computational Intelligence Society is most pleased to announce that Marios Polycarpou has been selected as the recipient of the prestigious 2023 IEEE Frank Rosenblatt Technical Field Award. Professor Polycarpou was selected "for pioneering contributions to the theory and application of neural networks and learning systems in monitoring and control." Professor Polycarpou will receive the award at the 2023 International Joint Conference on Neural Networks (IJCNN 2023), which will be held in Queensland, Australia, from 18-23 June 2023.

Marios Polycarpou’s research on intelligent monitoring and control has had a long-lasting impact in numerous application domains, including water distribution networks, robotic systems, smart buildings, automation in healthcare devices, smart electric grids, and transportation. His work in neural networks-based adaptive control methods has transformed the field and opened new ways for designing neural control schemes with learning capabilities for uncertain dynamical systems. He also initiated an entirely new approach to using a learning systems methodology in fault diagnosis. First developed in 1994, his approach is now important to the Internet of Things and cyber-physical systems, where faults can heavily—and subtly—affect application performance.

An IEEE Fellow, Polycarpou is a professor of electrical and computer engineering and director of the KIOS Center of Excellence, at the University of Cyprus, Nicosia, Cyprus.

The IEEE Frank Rosenblatt Award was established in 2004.

The award is named in honor of Frank Rosenblatt who is widely regarded as one of the founders of neural networks. Basing his research on the study of fly vision, he developed the single-layer input layer and an output layer of neural cells. Frequent presentation of a pattern or patterns resulted in changes in the input to output connections, facilitating future recognition of these patterns, or memory.  His work influenced and even anticipated many modern neural network approaches.

Recipient selection is administered through the Technical Field Awards Council of the IEEE Awards Board.

Presented to: An individual, multiple recipients, or a team of not more than three.

Scope: For outstanding contributions to biologically and linguistically motivated computational paradigms and systems.

Prize: The award consists of a bronze medal, certificate, and honorarium.

Basis for judging: In the evaluation process, the following criteria are considered: quality of the contribution; impact on the technical field and society as demonstrated by publications, patents, or other evidence; and the quality of the nomination.

Nomination deadline: endorsement letters are due 31 January annually.

Notification: Recipients are typically approved during the June IEEE Board of Directors meeting, usually held towards the end of the month. Recipients and their nominators will be notified following the meeting. Subsequently, the nominators of unsuccessful candidates will be notified of the status of their nomination.

Presentation: IEEE policy requires that its awards be presented at major IEEE events that are in keeping with the nature of the award and the cited achievement.

Sponsor: IEEE Computational Intelligence Society

Eligibility: Please be sure to review the eligibility requirements to understand if your candidate is eligible to be nominated.

To submit a nomination please visit the IEEE Technical Fields Awards page. Nominations must be finalized by 15 January 2023. 

Choosing Representation, Mutation, and Crossover in Genetic Algorithms

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 interactive components for a hands-on exploration of the covered material. Read More

IEEE Computational Intelligence Magazine, November 2022

Development of an Indoor Exergame Based on Moving-Target Hitting Task for COVID-19 Epidemic: A Comparison Between AR and VR Mode

The COVID-19 has led to home quarantine in global scale, which increased people’s need for indoor fitness activities in order to relieve stress/anxiety and learn knowledge well during the prevention. In this article, an augmented reality (AR) indoor exergame is developed, mainly a kind of moving target hitting task with Lomachenkos headwear speedball as the prototype. Then, a comparative experiment is performed to test the effect of the exergame in relieving anxiety, as well as the differences between AR and virtual reality modes (using HTC Vive Pro) on user experience (flow experience and motion sickness) and interaction efficiency. Read More

IEEE Transactions on Games, September 2022

Decision Making in Monopoly Using a Hybrid Deep Reinforcement Learning Approach

Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires players to make multiple decisions during the game. Decision-making in Monopoly involves many real-world elements such as strategizing, luck, and modeling of opponent’s policies. In this paper, we present novel representations for the state and action space for the full version of Monopoly and define an improved reward function. Using these, we show that our deep reinforcement learning agent can learn winning strategies for Monopoly against different fixed-policy agents. In Monopoly, players can take multiple actions even if it is not their.  Read more

IEEE Transactions on Emerging Topics in Computational Intelligence, December 2022

Sparkle: Toward Accessible Meta-Algorithmics for Improving the State of the Art in Solving Challenging Problems

Many fields of computational science advance through improvements in the algorithms used for solving key problems. These advancements are often facilitated by benchmarks and competitions that enable performance comparisons and rankings of solvers. Simultaneously, meta-algorithmic techniques, such as automated algorithm selection and configuration, enable performance improvements by utilizing the complementary strengths of different algorithms or configurable algorithm components. In fact, meta-algorithms have become major drivers in advancing the state of the art in solving many prominent computational problems. Read More

IEEE Transactions on on Evolutionary Computation, December 2022

Recent Advances in Trustworthy Explainable Artificial Intelligence: Status, Challenges, and Perspectives

Artificial intelligence (AI) and machine learning (ML) have come a long way from the earlier days of conceptual theories, to being an integral part of today’s technological society. Rapid growth of AI/ML and their penetration within a plethora of civilian and military applications, while successful, has also opened new challenges and obstacles. With almost no human involvement required for some of the new decision-making AI/ML systems, there is now a pressing need to gain better insights into how these decisions are made. This has given rise to a new field of AI research, explainable AI (XAI). In this article, we present a survey of XAI characteristics and properties. Read More

IEEE Transactions on Artificial Intelligence, December 2022

A Predictive Coding Account for Cognition in Human Children and Chimpanzees: A Case Study of Drawing

Humans and chimpanzees differ in their cognitive abilities, in particular, in social-cognitive processing; however, the underlying neural mechanisms are still unknown. Based on the theory of predictive coding, we hypothesize that crucial differences in cognitive processing might arise from aberrant reliance on predictions. We test this hypothesis using a recurrent neural network that integrates sensory information with predictions based on the rules of Bayesian inference. Altering a network parameter, we vary how strongly the network relies on its predictions during development. Read More

IEEE Transactions on Cognitive and Developmental Systems, December 2022

Iteratively Reweighted Algorithm for Fuzzy c-Means

Fuzzy c -means method (FCM) is a popular clustering method, which uses alternating iteration algorithm to update membership matrix F and center matrix M of d×c size. However, original FCM suffers from finding a suboptimal local minimum, which limits the performance of FCM. In this article, we propose a new optimization method for fuzzy c -means problem. We first propose an equivalent minimization problem of FCM, then, a simple alternating iteration algorithm is proposed to solve the new minimization problem, which involves an effective and theoretically guaranteed Iteratively Reweighted (IRW) method, so we call the new optimization method IRW-FCM.Read More

IEEE Transactions on Fuzzy Systems, October 2022

• IEEE TCDS Special Issue on Movement Sciences in Cognitive Systems (6 January 2023)

• IEEE CIM Special Issue on Artificial Intelligence eXplained (AI-X) (31 January 2023)

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

• IEEE TNNLS Special Issue on Deep Learning for Intelligent Media Computing and Applications (31 January 2023)

• IEEE TETCI Special Issue on Resource Sustainable Computational and Artificial Intelligence (1 February 2023)

• IEEE TETCI Special Issue on Trusted Mobile Crowdsourcing for Next Generation Intelligent Transportation Systems (1 February 2023)

• IEEE TNNLS Special Issue on Graphic Learning (1 March 2023)

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

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. Access all submission details here.

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. The paper submission deadline is 13 January 2023. More details can be seen on the paper submission page.