UK Workshop on Computational Intelligence

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September 9-11, 2013, University of Surrey, Guildford, United Kingdom




Keynotes:

 

  1. Prof Andy Adamatzky, UWE Bristol UK (http://uncomp.uwe.ac.uk)
    Andy Adamatzky


    Title: Physarum Chip: Towards Slime Mould Computers

    Abstract: Plasmodium of acellular slime mould Physarum polycephalum is a gigantic single cell visible by unaided eye. The cell shows a rich spectrum of behavioural patterns in response to environmental conditions. In a series of simple laboratory experiments we illustrate how to make computing, sensing and actuating devices from the slime mould. We show how to program living slime mould machines by configurations of repelling and attracting gradients and demonstrate workability of the living machines on tasks of computational geometry, logic, and arithmetic.

    Biography: Andrew Adamatzky is Professor in the Department of Computer Science and Director of the Unconventional Computing Centre, University of the West of England, Bristol, UK. He does research in reaction-diffusion computing, cellular automata, physarum computing, massive parallel computation, applied mathematics, collective intelligence and robotics, bionics, computational psychology, non-linear science, novel hardware, and future and emergent computation.


  2. Prof. Jun Wang, Department of Mechanical & Automation Engineering
    Jun Wang The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
    http://spring.mae.cuhk.edu.hk/~jwang/

    Title: The State of the Art of Neurodynamic Optimization – Past, Present, and Prospect

    Abstract: Optimization problems arise in a wide variety of scientific and engineering applications. It is computationally challenging when optimization procedures have to be performed in real time to optimize the performance of dynamical systems. For such applications, classical optimization techniques may not be competent due to the problem dimensionality and stringent requirement on computational time. One attractive approach is neurodynamic optimization based on recurrent neural networks. Because of the inherent nature of parallel and distributed information processing in neural networks, the convergence rate of the solution process is not decreasing as the size of the problem increases. Neural networks can be implemented physically in designated hardware such as ASICs where optimization is carried out in a truly parallel and distributed manner. This feature is particularly desirable for dynamic optimization in decentralized decision-making situations. In this talk, we will present the historic review and the state of the art of neurodynamic optimization models and selected applications. Specifically, starting from the motivation of neurodynamic optimization, we will review various recurrent neural network models for optimization. Theoretical results about the stability and optimality of the neurodynamic optimization models will be given along with illustrative examples and simulation results. It will be shown that many computational problems in science and engineering can be readily solved by means of neurodynamic optimization. In addition, prospective future research directions will be discussed.

    Biography: Jun Wang is a Professor and the Director of the Computational Intelligence Laboratory in the Department of Mechanical and Automation Engineering at the Chinese University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, and University of North Dakota. He also held various short-term visiting positions at USAF Armstrong Laboratory (1995), RIKEN Brain Science Institute (2001), Universite Catholique de Louvain (2001), Chinese Academy of Sciences (2002), Huazhong University of Science and Technology (2006–2007), and Shanghai Jiao Tong University (2008-2011) as a Changjiang Chair Professor. Since 2011, he is a National Thousand-Talent Chair Professor at Dalian University of Technology on a part-time basis. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published 160 journal papers, 13 book chapters, 8 edited books, and numerous conference papers in these areas. He has been an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics – Part B since 2003 and a member of the editorial board or editorial advisory board of Neural Networks and International Journal of Neural Systems. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009) and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence. He was also an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012). He is an IEEE Fellow, IAPR Fellow, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011.


  3. Prof. Dr. Bernhard Sendhoff, Honda Research Institute Europe, Germany
    Bernhard Sendhoff
    Title: Computational Intelligence for Engineering Design

    Abstract: Engineering design is a complex process that involves many different disciplines and objectives. In my presentation, I will outline how we have used methods from computational intelligence at the Honda Research Institute Europe to augment the “conventional” engineering design process with a focus on surface design. Apart from increasing the quality of the design, it is also the aim to explore regions in the design space that can give the engineer new insights. In engineering design, data is generated from many sources. Using new techniques from data analytics we can extract knowledge about the design and about the process. This information can be fed back into the design framework and can be visualized interactively for the engineer. The next research challenges in computational intelligence for engineering design will be discussed at the end of my presentation.

    Biography: Bernhard Sendhoff obtained a PhD in Applied Physics in May 1998, from the Ruhr-Universität Bochum, Germany. From 1999 to 2002 he worked for Honda R&D Europe GmbH, and since 2003, he has been with the Honda Research Institute Europe GmbH. Since 2007 he is Honorary Professor of the School of Computer Science of the University of Birmingham, UK. Since 2008, he is Honorary Professor at the Technical University of Darmstadt, Germany. Since 2011 he is President of the Honda Research Institute Europe GmbH. Bernhard Sendhoff is a senior member of the IEEE and a senior member of the ACM. His research focuses on methods from computational intelligence and their applications in development, production and services. He has authored and co-authored over 150 scientific papers and over 30 patents