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Keynote Speakers

  • David Wolfe Corne, Heriot Watt University, UK
  • Hideyuki Takagi , Kyushu University, Japan
  • B. Jayaram, Indian Institute of Technology, Delhi, India
  • Dipankar Dasgupta, University of Memphis, USA
  • Mario Koeppen, Kyushu Institute of Technology, Japan
  • Gauri Mittal, University of Guelph, Canada
  • Kamala Krithivasan, Indian Institute of Technology, Madras, India
  • Hendrik Richter, Leipzig University of Applied Sciences, Germany


David Wolfe Corne, Heriot Watt University, UK

Title: Super-Heuristics: Evolving problem solvers

Abstract: A small number of independent strands of research since the 1960s have explored ways to automatically combine individual heuristics to help solve problem instances, or in some cases to produce new algorithms. The current buzzword "hyper-heuristics" arises from this activity. Hyper-heuristics is largely concerned with manipulating the order of execution of individual heuristics (e.g. such as dispatch rules in scheduling, or first-fit, best-fit, and so on, in bin-packing) in search for the best solutions to a given problem instance. Often, bio-inspired
methods, or reinforcement learning methods, are used to do the manipulation. A small portion of this overall activity is, in some contrast, concerned with the development of new algorithms --i.e. combinations of low-level heuristics that not only solve a given instance, but are reusable for a class of instances. To distinguish this type of activity, which I believe is particularly promising, and far more interesting than using hyper-heuristics for solving single instances, I call it Super-heuristics.  Super-heuristics has achieved some notable successes so far, and could lead to enormously more efficient and effective optimization in certain areas of industry.  In this talk I will try to characterize the state of the art in super-heuristics, and set out several potentially promising ways forward.

Biography: David is Director of Research for the School of Mathematics and Computer Sciences (MACS) at Heriot-Watt University, Edinburgh, UK. MACS is pre-eminent in Scotland in the Mathematical and Computer Sciences, compromising part of the Maxwell Institute for Mathematical Sciences (a joint institute with the University of Edinburgh), and a number of pioneering research centers. He also leads the Intelligent Systems Laboratory, which maintains a portfolio of substantial achievements that range through fundamental models of computation, computational systems biology, computational neuroscience, and advanced methods for design, optimization and data mining. Following several years as a research associate in the Department of Artificial Intelligence at the University of Edinburgh, which led to the extremely successful problem solving approach now called `hyper-heuristics', David was a Lecturer (1997) and then Reader (2003) at the University of Reading. He then took up a Chair in Computer Science at the University of Exeter in 2004, and moved to his current post in 2006. His continuing research agenda concerns novel methods for optimization, data mining and machine learning, as well as strategies for solving large scale problems, with particular interests in multicriteria problems.  He serves on the editorial boards of many prestigious journals, including Natural Computation, Theoretical Computer Science (C), the AI Review Journal, the IEEE/ACM Trans on Computational Biology and Bioinformatics and the IEEE Trans on Evolutionary Computation. He thinks citation counts are a very poor and damaging way to evaluate science, but, in case you are interested, he has a g index of 67 and an H index of 31.


Hideyuki Takagi , Kyushu University, Japan

Title: Recent Topics in IEC Research

Abstract: The first topic of this talk is to show new types of Interactive Evolutionary Computation (IEC) application researches. Major IEC applications are optimizing target systems and creating graphics, images, shapes, sounds, vibrations, and others. We introduce two new types of IEC applications. The first one is measuring human characteristics. IEC is an optimization method based on human subjective evaluation. Likely reverse engineering, we may measure the evaluation characteristics or mental conditions of an IEC user by analyzing the outputs from the target system optimized by the user. The second one is extension of IEC
evaluation. Usually IEC optimizes a target system based on IEC user's subjective evaluation, i.e. psychological evaluation. We may extend the evaluation from psychological one to physiological one. We show the framework of the extended IEC.

The second topic of this talk is to overview researches that try to reduce IEC user fatigue and show our latest research in this area. Several approaches have been proposed to reduce IEC user's fatigue; some of them are improving input/output interface, accelerating EC search, allowing human intervention into EC search, estimating human evaluations, and others. Here, we introduce our latest research and show our view.

Biography: Hideyuki Takagi worked for the Central Research Laboratories of Panasonic Corporation in 1981 - 1995, and was a visiting researcher at UC Berkeley in 1991 - 1993 hosted by Prof. L. A. Zadeh. He moved to Kyushu Institute of Design in 1995 as an Associate Professor and now works for Kyushu University since both universities merged in 2003. He had worked on cooperating neural networks, fuzzy systems, and genetic algorithms and now is focusing on Humanized Computational Intelligence with interactive evolutionary computation. Prof. Takagi has worked for IEEE SMC Society as the Vice-President, the Chair of Technical Committee on Soft Computing, and an Associate Editor of IEEE Trans. on SMC-B. See Detail Profile at: http://www.design/kyushu-u.ac.jp/~takagi


B. Jayaram, Indian Institute of Technology, Delhi, India

Title: Gene to Drug in Silico: A Molecular Approach

Abstract: The world wide genome sequencing efforts and the concurrent developments in scientific software implementations on massively parallel computer architectures grant us the opportunity to dream that drug design could be undertaken against suitable biomolecular targets to develop individualized medicine almost in an automated way. Currently however, without the help of any database, an inspection of a DNA sequence does not tell us whether it is likely to be a gene and if it is a gene for messenger RNA, what the likely three dimensional structure of its protein product is. Also drug design softwares fall short of expectations even if the structures of drug targets are known.
Addressing the above issues from a physico-chemical perspective, we have developed a novel semi-empirical physico-chemical model for whole genome analysis (ChemGenome) based on DNA energetics, an all atom energy based computational protocol for narrowing down the search space for locating tertiary structures of small globular proteins (Bhageerath) and a binding free energy based methodology for active site directed lead molecule design (Sanjeevini). The ChemGenome could distinguish genes from non-genes in 331 bacterial genomes and 20 eukaryotic genomes with > 90% accuracy. Bhageerath could successfully bracket native-like structures to within 3 to 7 Ã… in the 5 lowest energy structures for 50 small alpha helical globular proteins. The Sanjeevini protocols could sort drugs from non-drugs for a few drug targets helping in addressing both affinity and specificity issues in drug design. Progresses recorded in the areas of genome analysis, protein structure prediction and drug design and the software tools developed and made freely accessible at www.scfbio-iitd.res.in together with challenges and promises there of will be presented.

1. (a) Dutta,S., Singhal,P., Agrawal,P., Tomer,R., Kritee, Khurana,E. and Jayaram.B. A Physico-Chemical Model for Analyzing DNA sequences, 2006, Journal of Chemical Information & Modelling, 46(1), 78-85. (b) Poonam Singhal, B. Jayaram, Surjit B. Dixit and David L. Beveridge. Molecular Dynamics Based Physicochemical Model for Gene Prediction in Prokaryotic Genomes, 2008, Biophysical Journal, 94, 4173-4183.

2. (a), Narang,P, Bhushan,K., Bose,S. and Jayaram,B. A computational pathway for bracketing native-like structures for small alpha helical globular proteins. 2005, Phys. Chem. Chem. Phys., 7, 2364.; (b) Narang,P, Bhushan,K., Bose, S., Jayaram,B. Protein structure evaluation using an all atom energy based empirical scoring function, 2006, J. Biomol. Struct. Dyn., 23, 385-4006. (c) Jayaram et al., Bhageerath, 2006, Nucleic Acid Res., 34, 6195-6204; (d) Jayaram, B.. Decoding the Design Principles of Amino Acids and the Chemical Logic of Protein Sequences. Available from Nature Proceedings. http://hdl.handle.net/10101/npre.2008.2135.1 2008

3. (a) Jain, T and Jayaram, B. An all atom energy based computational protocol for predicting binding affinities of protein-ligand complexes. 2005, FEBS Letters, 579, 6659; (b) Jain, T and Jayaram, B. A computational protocol for predicting the binding affinities of zinc containing metalloprotein-ligand complexes. 2007, Proteins: Structure, function & Bioinformatics, 67, 1167-1178; (c) Shaikh, S., Jayaram. B., A swift all atom energy based computational protocol to predict DNA-Drug binding affinity and Tm, 2007, J. Med. Chem., 50, 2240-2244; (d) Shaikh, S., Jain. T., Sandhu, G., Latha, N., Jayaram., B., A physico-chemical pathway from targets to leads, 2007, Current Pharmaceutical Design, 13, 3454-3470.

Biography: (i) Ph.D. (1986) City Univ. of New York, USA.
(ii) Post Doctoral Fellow (1987-88) Columbia University, USA.
(iii) Senior Research Associate (1989-90): Wesleyan University, USA.
(iv) Faculty of the Department of Chemistry, IIT Delhi since 1990.
(Assistant Professor from 1990-95, Associate Professor from 1995-1999 & full Professor Since 2000.
(v) Recipient of Chemical Research Society of India Medal (2000)
(vi) Principal Investigator & Coordinator of Supercomputing Facility for Bioinformatics & Computational Biology, IIT Delhi (Since 2001).
(vii) Member of the National Task Force on Bioinformatics of Department of Biotechnology (since 1998)
(viii) Member of Physical Chemistry Programme Advisory Committee of the Department of Science & Technology (2004 -2006)
(ix) Member of Organic Chemistry Programme Advisory Committee of the Department of Science & Technology (2007-2009).
(x) Vice President of Indian Biophysical Society (2006-2008).
(xi) Member of DIT’s Working Group on Bioinformatics (since 2007)
(xii) Head, Department of Chemistry, IIT Delhi (Sept. 2006- Aug. 2009).
(xiii) Coordinator, School of Biological Sciences, IIT Delhi (since 2008).
(xiv) Member of National Committee of IUPAB (2008-2010).
(xv) Member of the Editorial Board of Journal of Molecular Graphics and Modeling (since 2009).


Dipankar Dasgupta, University of Memphis, USA

Abstract: The biological immune system (BIS) is a highly parallel and distributed adaptive system. It uses feature extraction, memory, diversity and associative retrieval to solve recognition and classification tasks. In particular, it learns to recognize relevant patterns, remember previously encountered patterns and use combinatorics to construct pattern detectors efficiently. These remarkable information-processing abilities of the immune system has inspired an emerging field, sometimes referred to as the Immunological Computation, Immuno-computing or Artificial Immune Systems (AIS) that extracts ideas from BIS to develop computational tools for solving science and engineering problems.
Over the last two decades, there has been an increased interest in immuno-inspired techniques and their applications. In general, some of such models are intended to describe immunological processes for a better understanding of the dynamical behavior of the BIS in the presence of antigens. On the other hand, immunity-based models have been developed in an attempt to solve wide variety of real-world problems. In particular, there exist a number of applications in pattern recognition, fault detection, computer security; also other applications currently being explored in science and engineering problem domain. This talk will cover the latest advances in Immunological approaches and a few real-world applications.

Biography: Dr. Dipankar Dasgupta is a Professor of Computer Science at the University of Memphis, Tennessee, USA. His research interests are broadly in the area of scientific computing, tracking real-world problems through interdisciplinary cooperation. His areas of special interests include Artificial Immune Systems, Genetic Algorithms, multi- agent systems and their applications. He published more than 145 papers in book chapters, journals, and international conferences. His recently published a graduate textbook on Immunological Computation, published by CRC press, September 2008. He also edited two books: one is on Genetic Algorithms and the other entitled "Artificial Immune Systems and Their Applications" published by Springer-Verlag, 1999. The book on Artificial Immune Systems is the first book in the field and widely use as a reference book. Dr. Dasgupta is a senior member of IEEE, ACM and regularly serves as panelist, keynote speaker and program committee member (5-6 per year) in many International Conferences. He first started (in 1997) organizing special tracks and workshops on Artificial Immune Systems (AIS) and regularly offered tutorials on the topics at International Conferences since then. He is an associate editor of three journals and also is the chair of IEEE Task Force on Artificial Immune Systems. 


Mario Koeppen, Kyushu Institute of Technology, Japan

Title: The (still) various challenges of Gestalt theory

Abstract: Gestalt and corresponding Gestalt laws of vision are apparent phenomena of visual perception that still lack general understanding, despite of passing more than 100 years after its first mentioning in psychological literature. In this contribution, we want to promote Gestalt as a kind of challenge to the naturally and biologically inspired computation community. Browsing a bulk of existing research literature on the Gestalt theme, with only a few notable exceptions (like the Helmholtz principle), there is not much indication for a comprehensive approach to the understanding of Gestalt, for having explanations about the means for its application, or for advancement in the provision of models reflecting the complex interplay of Gestalt laws in a verificable manner. Said this, currently Gestalt triggers more questions than answers, and it might slowly become obvious that Gestalt is more than being just a source of inspiration for new algorithms, or for stimulating modifications of existing algorithms. It also gets slowly more clear that the only open issue is not just a lack of "holistic view" in present science, as it is often stated. It seems that any further progress in this regard might require a more rigorous departure from existing computational paradigms and concepts than expected.

In this talk, the state of research on Gestalt in engineering sciences, esp. image processing and pattern analysis, will be critically reviewed, and their strong and weak points will be evaluated. But moreover, new emerging computational paradigms and models will be evaluated according to what they might provide to the understanding of Gestalt. Among these paradigms and models, we can find the Neural Darwinism, which relates evolutionary concepts to the processing of the brain, or the recently proposed Cogency Confabulation, which relates learning with the maximization of a priori probability, and which is accompanied by a novel neural network architecture. But going further, Gestalt guides to rather more fundamental issues of general system modelling, and some relations to unconventional biological theories like Rosen's MR-systems will be reflected as well.

Biography: Mario Koeppen was born in 1964. He studied physics at the Humboldt-University of Berlin and received his master degree in solid state physics in 1991. Afterwards, he worked as scientific assistant at the Central Institute for Cybernetics and Information Processing in Berlin and changed his main research interests to image processing and neural networks. From 1992 to 2006, he was working with the Fraunhofer Institute for Production Systems and Design Technology. He continued his works on the industrial applications of image processing, pattern recognition, and soft computing, esp. evolutionary computation. During this period, he achieved the doctoral degree at the Technical University Berlin with his thesis works: "Development of an intelligent image processing system by using soft computing" with honors. He has published around 100 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as chair or member of the program committee, incl. the WSC on-line conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is founding member of the World Federation of Soft Computing, and also member of the editorial board of the Applied Soft Computing journal, the Intl. Journal on Hybrid Intelligent Systems and the Intl. Journal on Computational Intelligence Reserach. In 2006, he became JSPS fellow at the Kyushu Institute of Technology in Japan, and in 2008 Professor at the Network Design and Reserach Center (NDRC) of the Kyushu Institute of Technology, where he is conducting now research in the fields of multi-objective optimization, digital convergence and multimodal content management.


Gauri Mittal, University of Guelph, Canada

Title: Advances in signal and image processing to detect contaminants and microorganisms in foods for safety and quality 

Abstract: Metal, plastics and glass fragments found in packaged bottles, packages and containers are big concern for food processors. Using ultrasound to detect these fragments in the containers poses a challenging task for signal processing and classification. Various novel signal processing techniques are developed and discussed. The center frequency pressure ratio (CFPR), variance, and backscattered amplitude integral (BAI) methods have approximately the same overall detection rating. Root mean square (RMS) method has the highest overall detection rating. A hybrid of these methods has significantly improved object detection rating. Image processing algorithm is based on the longitudinal (vertical) tracing of a center frequency component obtained using short-time Fourier Transform (STFT) in conjunction with a transversal (horizontal) differentiation of the image pixels. This method has improved ability to detect small glass fragments inside the bottle.  Radial Basis Function Neural Network (RBF-NN) is used for signal classification. The output of the RBF layer is determined by the distance between the input vector and its centroid vector. Successful classification rate of 95% was achieved using RBF-NN method.

A rapid and cost effective technique for identification and classification of microorganisms was explored using fluorescence microscopy and image analysis. After staining the microorganisms with fluorescent dyes (diamidino-2-phenyl-indole (DAPI) and acridine orange, AO), images of the microorganisms were captured using a CCD camera attached to a light microscope. Geometrical, optical and textural features were extracted from the images using image analysis. From these parameters, the best identification parameters that could classify the microorganisms with higher accuracy were selected using a probabilistic neural network (PNN). PNN was then used to classify the microorganisms with a 100% accuracy using those identification parameters. 

Biography: Dr. G. S. MITTAL is a Professor of systems and food engineering at the School of Engineering, University of Guelph, Guelph, Ontario, Canada. He is an author of more than 250 refereed journal research papers and 210 other publications, as well as three books such as ”Computerized Controls in Food Industry”. He is the recipient of the 1994 John Clark Award presented by the Canadian Society of Biological Engineering, the 1994 Membro Benemerito Award given by the Colombian Association of Food Engineers, and International Best Researcher award 2005 & 2007 by Japanese Association of Food Machinery Manufacturers. A registered professional engineer, professor Mittal received the B.Sc. (engineering) from India (1969); the M.Sc. (1976) from the University of Manitoba, Canada, and the Ph.D. (1979) from the Ohio State University, USA. Mittal presently conducting research in the areas of system modelling, simulation and optimization; high voltage pulsed electrical field; sensor and detection systems development; neural network; image processing; and robotics in food processing. Mittal is an internationally recognized food engineer with more than 30 years of professional experience. He was an organizing member of many international symposiums and conferences; and invited by many universities and research institutes worldwide for conducting workshops, lectures and keynote addresses on varied topics. He has provided consulting services in systems and food engineering areas to many industries in the last 30 years.


Kamala Krithivasan, Indian Institute of Technology, Madras, India

Title: Membrane Computing

Abstract: ‘Natural Computing’ is an area which is pursued with interest in recent times.  ‘Cellular computing’ is a part of Natural Computing.  One model of cellular computing is membrane computing (P Systems) initiated by Gh. Paun in 1998, aiming at devising a computing model inspired by the structure and functioning of living cells.  It is a parallel and distributed model of computing.
    In the basic model of P systems, one considers a membrane structure consisting of several cell-like membranes which are hierarchically embedded in a main membrane, called the skin membrane.  Membranes with no other membranes embedded in them are called elementary. The membranes delimit regions, where we place objects.  The objects evolve in nondeterministic maximally parallel manner according to given evolution rules.  The objects can also be described by strings and then they are processed by string operations.  Starting from an initial configuration, identified by membrane structure and objects in all regions, and using evolution rules, we get a computation.  We consider a computation complete when it halts, i.e., no further rule can be applied.  The result of the halting computation is defined on the basis of the objects we obtained in a specified membrane or expelled from the skin membrane. We may look at membranes to correspond to regions in cells and objects to chemical compounds and use rules to describe the evolution of those chemical compounds. In this talk some basic concepts about membrane computing will be discussed and some variations of the basic model explained.  The talk will also touch upon a new variant ‘Spiking Neural P Systems’.


Hendrik Richter, Leipzig University of Applied Sciences, Germany

Title: Evolutionary optimization of dynamic fitness functions

Abstract: Many real world optimization problems are dynamic, which means that the fitness function changes with time and that the time scale of these changes is in the same magnitude as the run-time of the evolutionary algorithm. It is well-known that evolutionary algorithms are remarkably successful in solving static optimization problems showing a high degree of problem difficulty. In recent years it further has been shown that these problem solving abilities can also be used to tackle dynamic optimization problems. However, certain modifications in the algorithmic structure of the evolutionary algorithm are necessary to make it work in dynamic fitness landscapes. Dynamic optimization means no longer to find one optimal solution, but to track the movement of the optimal solution with time. This situation means that the algorithm must be equipped with some additional schemes which can control, maintain and occasionally enhance the population's diversity. In the talk I will discuss recent results in designing evolutionary algorithms that are fit to perform in dynamic environments. This will cover approaches dealing with the diversity issue namely random diversity enhancement schemes, memory schemes and anticipation/prediction methods. Another topic that will be addressed is the question of how to find out if the fitness function has changed, also know as the change detection problem.

Biography: Dr. Hendrik Richter is Professor of Control Engineering at the School of Electrical Engineering & Information Technology, Leipzig University of Applied Sciences (HTWK Leipzig). His research interests are in two main areas, one is nonlinear dynamics and chaos, the other is nature-inspired problem solving, particularly evolutionary computation. Both areas overlap in the topic of designing evolutionary algorithms for solving dynamic optimization problems. He has serves in the programme committee of the European Events on Evolutionary Computation in Stochastic and Dynamic Environments (EvoSTOC) and of the IEEE Congress on Evolutionary Computation’s Special Session on Evolutionary Computation in Dynamic and Uncertain Environments (ECiDUE) for about 5 years.

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