Click here for more details

The IEEE Computational Intelligence Society sponsors many conferences each year with varying levels of financial sponsorship. We are currently looking for proposals for the 2026 World Congress on Computational Intelligence (WCCI 2026). WCCI combines the Society’s flagship conferences for evolutionary computation and fuzzy systems with the International Joint Conference on Neural Networks. IEEE WCCI 2024 will be held in Japan, we hope that some bids from other geographical will be submitted.

Proposals for the IEEE WCCI 2026 conference proposals should be submitted by 30 June 2023 (hard deadlines) to provide sufficient time for review and decision by IEEE CIS Confcom and Adcom. Please inform VP Conferences Marley Vellasco ([email protected]) and Steven Corns ([email protected]) as Chair of the Confcom Subcommittee on Future Conferences 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. 

More information on DSAA and the call for bids is available here.

Meet: Nelishia Pillay

Volunteer Roles:

• Chair of the IEEE CIS Women in Computational Intelligence subcommittee
• Chair of the IEEE CIS Mentoring Program subcommittee
• Chair of the IEEE CIS Outstanding PhD Dissertation Award
• IEEE CIS Society Coordinator on IEEE WIE
• IEEE CIS Liaison to the IEEE TAB CDI

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

• Professor (Computer Science)
• SARChI Co-Funded Chair in Artificial Intelligence for Sustainable Development
• Multichoice Joint Chair in Machine Learning

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

I have been a member for 5 years. CIS is a leading society in my research area, computational intelligence, and I joined to improve my productivity in the field and keep up to date with current trends.

What Computational Intelligence society committee do you serve?

Currently, I am the Chair of the IEEE CIS Women in Computational Intelligence subcommittee, Chair of the IEEE CIS Mentoring Program subcommittee, Chair of the IEEE CIS Outstanding PhD Dissertation Award, IEEE CIS Society Coordinator on IEEE WIE ­ 2022­ and 2023, IEEE CIS Liaison to the IEEE TAB CDI ­ 2023, and member of several other committees.

Previously, I served as the Chair of the IEEE Intelligent Systems Applications Technical Committee, Co-­Leader of the IEEE CEC 2022 Mentoring Programming, Member of IEEE CIS Outstanding Phd Dissertation Award, IEEE Computational Intelligence Magazine Ad­Hoc Plagiarism Committee and many other committee or subcommittees.

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

Attending IEEE CIS technical events has made me aware of advancements in the field as well as gaps needing further research, thus enabling me to focus my research and improving the impact and contribution made. Serving as a volunteer has helped develop my soft skills by interacting with people at different levels and these skills are invaluable whether supervising students or working with collaborators. Support and encouragement from Senior members of the Society has given me the confidence to achieve things I would have never believed I could.

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

For me the most rewarding aspects about being a volunteer are:

1) Providing support for members of our community to achieve their goals.

2) Using our CIS expertise to make a difference in society and ultimately improve the quality of life.

It is fantastic being part of a community where colleagues become good friends that you look forward to meeting and interacting with at Society events.

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

I love spending time on nature trails, jogging and walking. In my younger years I had a passion for martial arts and was fortune to get the opportunity to represent my country at world championships.

2023 Graduate Student Research Grants Awarded

The CIS Graduate Student Research Grants subcommittee is happy to announce that 6 scholarships have been awarded to support research visits for collaborations in fields related to Computational Intelligence. Congratulations to the following successful applicants:

• Jianjun Chai for the project "Stable and Efficient Communication for Large-Scale Multi-Agent Reinforcement Learning"
• Angela Chiatto for the project "EVOlutionary algorithms toolbox for VAriational Quantum circuits (EVOVAQ)"
• Meijiang Gui for the project "Fusing Prior Analytical Knowledge into Neural Network for Tactile Information Representation"
• Chengpeng Hu for the project "Distilling Evolutionary Reinforcement Learning via Cooperative Co-evolution"
• Zeqiong Lv for the project "Theoretical Analysis of Evolutionary Neural Architecture Search Algorithms"
• Xinming Shi for the project "Evolving Analog Circuits Using Interpretable Genetic Programming with Semantic Diversity"

2023 IEEE CIS Summer Schools

We are excited to announce this year's highly anticipated list of IEEE CIS Summer Schools! Given so many great proposals, we have curated an exceptional lineup of educational programs designed to empower students and professionals in the field of Computational Intelligence. IEEE CIS is committed to fostering knowledge exchange, innovation, and professional development within the realm of computational intelligence. Summer Schools offer a unique opportunity to immerse yourself in cutting-edge topics, interact with renowned experts, and connect with like-minded individuals from around the world.

Checkout this year's list of summer schools and stay tuned for updates on the registration.

Adaptive Auxiliary Task Selection for Multitasking-Assisted Constrained Multi-Objective Optimization

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 is challenging and problem-dependent. This work presents a general multitasking framework comprising a main task for the original CMOP and an arbitrary number of auxiliary tasks based on different strategies for the auxiliary problems. Read More

IEEE Computational Intelligence Magazine, May 2023

Learning a Low-Dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems

For the safe application of reinforcement learning algorithms to high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning (SRL) framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and used to provide safety estimates for learning algorithms. However, finding a satisfying simplified system model for complex dynamical systems usually requires a considerable amount of effort. To overcome this limitation, we propose a general data-driven approach that is able to efficiently learn a low-dimensional representation of the safe region. Read More

IEEE Transactions on Neural Networks and Learning Systems, May 2023

More Than Accuracy: A Composite Learning Framework for Interval Type-2 Fuzzy Logic Systems

In this article, we propose a novel composite learning framework for interval type-2 (IT2) fuzzy logic systems (FLSs) to train regression models with a high accuracy performance and capable of representing uncertainty. In this context, we identify three challenges, first, the uncertainty handling capability, second, the construction of the composite loss, and third, a learning algorithm that overcomes the training complexity while taking into account the definitions of IT2-FLSs. This article presents a systematic solution to these problems by exploiting the type-reduced set of IT2-FLS via fusing quantile regression and deep learning (DL) with IT2-FLS. The uncertainty processing capability of IT2-FLS depends on employed center-of-sets calculation methods, while its representation capability is defined via the structure of its antecedent and consequent membership functions.  Read more

IEEE Transactions on Fuzzy Systems, March 2023

Promoting Transfer of Robot Neuro-Motion-Controllers by Many-Objective Topology and Weight Evolution

The ability of robot motion controllers to quickly adapt to new environments is expected to extend the applications of mobile robots. Using the concept of transfer optimization, this study investigates the capabilities of neuro-motion-controllers, which were obtained by simultaneously solving several source problems, to adapt to target problems. In particular, the adaptation comparison is carried out between specialized controllers, which are optimal for a single source motion problem, and nonspecialized controllers that can solve several source motion problems. Read More

IEEE Transactions on Evolutionary Computation, April 2023

Video Game Automated Testing Approaches: An Assessment Framework

The video-game industry has recently grown from focused markets to mainstream. The advancements the industry has been enjoying motivated researchers to propose techniques and tools to support the activities across the different phases of the game development lifecycle. Game testing is one of the crucial activities within the game development process. Due to the nature of game testing, many automated game testing techniques have been proposed in the literature. However, there is no framework that could be used to aid practitioners in selecting appropriate techniques suitable for their particular development efforts. Read More

IEEE Transactions on Games, March 2023

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloud-centric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL) Read More

IEEE Transactions on Cognitive and Developmental Systems, March 2023

A Cerebellum-Inspired Network Model and Learning Approaches for Solving Kinematic Tracking Control of Redundant Manipulators

Tracking control of redundant manipulators is always a basic and important issue in robotics. Existing studies have indicated that the pivotal region of the brain associated with human motion control is the cerebellum. This motivates us to devise a model-based cerebellum-inspired (MBCI) scheme and a model-free cerebellum-inspired (MFCI) scheme for the tracking control of redundant manipulators in this article. The MBCI scheme solves the inverse kinematics problem with a cerebellum model. By using the parameters and Jacobian matrix of the manipulator, the task space error is transformed into joint space error, which is taken as the teaching signal to train the cerebellum model designed based on the echo state network. The MFCI scheme is formed by coupling a cerebellum model and a multilayer perceptron (MLP). Read More

IEEE Transactions on Emerging Topics in Computational Intelligence, March 2023

• IEEE TEVC Special Issue on Evolutionary Neural Architecture Search (30 June 2023)
• IEEE TNNLS Special Issue on Graph Learning (1 July 2023)
• IEEE TETC Special Issue on Computational Intelligence in Mental Health (1 July 2023)
• IEEE TETC Special Issue on Data and Knowledge-assisted Evolutionary Computation (31 July 2023)
• IEEE TFS Special Issue on Fuzzy Systems Toward Human-Inspired Computational Intelligence in Complex Systems (31 October 2023)
• IEEE TFS Special Issue on Smart Fuzzy Optimization in Sustainable Manufacturing and Supply Chain Management (1 November 2023)
• IEEE TFS Special Issue on Deep Neuro-Fuzzy Approaches for Intelligent Big Data Processing (30 December 2023)
• IEEE TFS Special Issue on Fuzzy-Deep Neural Network Learning in Sentiment Analysis (15 January 2024)