2018 IEEE CIS Neural Networks Pioneer award to Prof. Lee Giles for pioneering role in the application of neural networks for information extraction in large data repositories.

2018 IEEE CIS Fuzzy Systems Pioneer award to Prof. Bernadette Bouchon-Meunier for influential contributions to fuzzy learning and reasoning.

2018 IEEE CIS Fuzzy Systems Pioneer award to Prof. Rudolf Kruse for fundamental contributions to fuzzy learning, theory and applications.

2018 IEEE CIS Evolutionary Computation Pioneer award to Prof. Kalyanmoy Deb for pioneering contribution, development, and leadership in evolutionary multi-criterion optimization.

2018 IEEE CIS Outstanding TNNLS Paper award to Qinglai Wei, Derong Liu, and Xiong Yang, "Infinite Horizon Self-Learning Optimal Control of Nonaffine Discrete-Time Nonlinear Systems," IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 4, pp. 866-879, Apr. 2015.

2018 IEEE CIS Outstanding TFS Paper award to Shaocheng Tong, Shuai Sui, and Yongming Li, “Fuzzy adaptive output feedback control of MIMO nonlinear systems with partial tracking errors constrained”, IEEE Transactions on Fuzzy Systems, vol.23, no.4, pp.729-742, 2015.

2018 IEEE CIS Outstanding TEVC Paper award to Xingyi Zhang, Ye Tian, Ran Cheng, and Yaochu Jin, “An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, 19(2): 201-213, April 2015.

2018 IEEE CIS Outstanding TAMD Paper award to Wei-Long Zheng, and Bao-Liang Lu, “Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks, IEEE Transactions on Autonomous Mental Development, vol. 7, no. 3, pp. 162-175, 2015.

2018 IEEE CIS Outstanding CIM Paper award to Gregory Ditzler, Manuel Roveri, Cesare Alippi and Robi Polikar, "Learning in Nonstationary Environments: A survey", IEEE Computational Intelligence Magazine, vol. 10, no. 4, pp. 12-25, Nov. 2015.

2018 IEEE CIS Outstanding Chapter award to Hyderabad Chapter for outstanding contributions to membership development by organizing events involving innovative ideas, keeping in view aspirations and needs of its members, Chapter Chair : Naresh kumar Mallenahalli

2018 IEEE CIS Outstanding PhD Dissertation award to Dr. Zhi-Hui Zhan, “Research into Machine Learning Aided Particle Swarm Optimization and Its Engineering Application,” South China University of Technology, China

2018 IEEE CIS Outstanding Early Career award to Dr. Ke Tang for contributions to the development, analysis and application of novel evolutionary computation approach for large-scale optimization.

Dear IEEE CIS members,

The following positions within the IEEE Computational Intelligence Society

(CIS) become vacant:

(1) VP for Finances (2019-20)

(2) VP for Conferences (2018-19)

(3) VP for Technical Activities (2018-19)

(4) Five ADCOM Members-at-Large (2018-2020)

According to the CIS Bylaws,

"Section 31.  Schedule for ADCOM Elections

Five ADCOM Members-at-Large are elected each year, plus any vacated positions.

"The election of Vice President for Conferences, Vice President for Finances,

and Vice President for Technical Activities shall take place in odd-numbered

years."

This email is the official call for nominations, including self-nominations,

for the above vacancies. The nomination materials should include the following:

(1) A brief CV of no more than 2 pages.

(2) A position statement of no more than 2 pages, explaining (a) your vision

of CIS's technical activities / finances / conferences (depending on

the position) and how they contribute to CIS's overall growth and serve

members' needs; (b) challenges faced by the CIS's technical activities /

finances / conferences and your ideas of tackling them;

(c) the objectives (has to be SMART) of your term (if elected) and beyond.

For candidates for ADCOM Members-at-Large, please explain in terms of your

general CIS vision, challenges, ideas and objectives.

(3) A list of previous volunteering experience within CIS, IEEE, and other

professional organisations, and relevant achievements. (No more than 2 pages)

All the nominations and self-nominations should be sent to the Chair of the CIS

Nominations Committee, me (x.yao@cs.bham.ac.uk) in this case, _and_ copy to

Jo-Ellen Snyder (j.e.snyder@ieee.org) by *** 21/05/2017 (Sunday) ***.

Please check CIS Bylaws for the nominee's eligibilty for different positions:

http://cis.ieee.org/bylaws.html. I will not cut and paste the long text here.

Best regards,

Xin Yao

Chair (2017) of CIS Nominations Committee

2018 IEEE World Congress on Computational Intelligence

2017 IEEE CIS Fuzzy Systems Pioneer award to Ching-Teng Lin for contributions in developing fuzzy neural networks with various learning abilities and their real-world applications

2017 IEEE CIS Evolutionary Computation Pioneer award to Kenneth Price and Rainer Storn for the initial development and research on differential evolution

2017 IEEE CIS Meritorious Service award to Xin Yao

2017 IEEE CIS Outstanding TNNLS Paper award to C. L. P. Chen, G. X. Wen, Y. J. Liu, and F. Y. Wang, “Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-delay Systems using Neural Networks,” IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 6, pp. 1217-1226, 2014

2017 IEEE CIS Outstanding TFS Paper award to Hui Zhang, Yang Shi and Junmin Wang, “On Energy-to-Peak Filtering for Nonuniformly Sampled Nonlinear Systems: A Markovian Jump System Approach,” IEEE Transactions on Fuzzy Systems, Vol. 22, No. 1, pp. 212-222, 2014

2017 IEEE CIS Outstanding TEVC Paper award to Kalyanmoy Deb and Himanshu Jain, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints,” IEEE Transactions on Evolutionary Computation, Vol. 18, No. 4, pp. 577-601, 2014

2017 IEEE CIS Outstanding TEVC Paper award to Mohammad Nabi Omidvar, Xiaodong Li, Yi Mei, and Xin Yao, “Cooperative co-evolution with differential grouping for large scale optimization,” IEEE Transactions on Evolutionary Computation, Vol. 18, No. 3, pp. 378-393, 2014

2017 IEEE CIS Outstanding TCIAIG Paper award to Richard Evans; Emily Short, “Versu—A Simulationist Storytelling System,” IEEE Transactions on Computational Intelligence and AI in Games, Vol. 6, No. 2, 113-130. 2014

2017 IEEE CIS Outstanding CIM Paper award to Zhi-Hua Zhou, Nitesh V. Chawla, Yaochu Jin, Graham J. Williams, “Big data opportunities and challenges: Discussions from data analytics perspectives,” IEEE Computational Intelligence Magazine, Vol. 9, No. 4, pp. 62-74, 2014

2017 IEEE CIS Outstanding Chapter award to Madras Chapter

2017 IEEE CIS Outstanding PhD Dissertation award to Daniel Leite, “Evolving granular systems,” University of Campinas, Brazil

2017 IEEE CIS Outstanding Organization award to British Telecom

2017 IEEE CIS Outstanding Early Career award to Dong Xu

IEEE FRANK ROSENBLATT AWARD—recognizes outstanding contributions biologically and linguistically motivated computational paradigms and systems—sponsored by the IEEE Computational Intelligence Society—to Recipient STEPHEN GROSSBERG (FIEEE)—Wang Professor of Cognitive and Neural Systems, Boston University, Boston, Massachusetts, USA Citation "For contributions to understanding brain cognition and behavior and their emulation by technology."

Click here for previous announcements!

The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017) will be held in Hawaiian Village Resort, Honolulu, Hawaii, USA. SSCI is a flagship annual international conference on computational intelligence sponsored by the IEEE Computational Intelligence Society, promoting all aspects of theory, algorithm design, applications and related emerging techniques. As a tradition, IEEE SSCI 2017 will co-locate a large number of exciting symposiums, each dedicated to a special topic within or related to computational intelligence, thereby providing a unique platform for promoting cross-fertilization and collaboration. SSCI 2017 will be featured by cross-symposium tutorials, keynote speeches, panel discussions, oral presentations and poster sessions.

• * Tutorial Proposals: March 10, 2017
• * Special Session Proposals: April 2, 2017
• * Paper Submissions: July 2, 2017
• * Early Registration: September 24, 2017
• * IEEE SCCI 2017: November 27-December 1, 2017

Smart Car, by S.-C. Huang, B.-H. Chen, S.-K. Chou, J.-N. Hwang, and K.-H. Lee, IEEE Computational Intelligence Magazine, 11(4):46-58, 2016.

DOI: 10.1109/MCI.2016.2601758
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7587488

The Smart Car is a highly computerized automobile featuring ubiquitous computing, intuitive human-computer interaction, and an open application platform. This paper proposes an advanced smart car demonstration platform with a transparent windshield display and various motion sensors where drivers can manipulate a variety of car-appropriate applications in augmented reality. Similar to smartphones, drivers can customize their smart cars through free downloads of car-appropriate applications. Three potential car-appropriate applications related to computer vision are investigated and implemented in the proposed platform for increasing driving safety. By embedding those car-appropriate applications, the smart car has the potential to increase the safety of driving conditions both in daytime and nighttime, even in bad weather.

Probe Machine, by J. Xu, IEEE Transactions on Neural Networks and Learning Systems, 27(7):1405-1416, 2016.

DOI: 10.1109/TNNLS.2016.2555845
URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7466831

In this paper, we present a novel computing model, called probe machine (PM). Unlike the turing machine (TM), PM is a fully parallel computing model in the sense that it can simultaneously process multiple pairs of data, rather than sequentially process every pair of linearly adjacent data. We establish the mathematical model of PM as a nine-tuple consisting of data library, probe library, data controller, probe controller, probe operation, computing platform, detector, true solution storage, and residue collector. We analyze the computation capability of the PM model, and in particular, we show that TM is a special case of PM. We revisit two NP-complete problems, i.e., the graph coloring and Hamilton cycle problems, and devise two algorithms on basis of the established PM model, which can enumerate all solutions to each of these problems by only one probe operation. Furthermore, we show that PM can be implemented by leveraging the nano-DNA probe technologies. The computational power of an electronic computer based on TM is known far more than that of the human brain. A question naturally arises: will a future computer based on PM outperform the human brain in more ways beyond the computational power?