Invited talks and tutorials
Invited talks and tutorials at LION5 have been confirmed by:
Prof. Benjamin W. Wah - "Planning Problems and Parallel
Decomposition: A Critical Look" 
Benjamin W. Wah is currently the Franklin W. Woeltge Endowed Professor of Electrical and Computer Engineering and Professor of the Coordinated Science Laboratory of the University of Illinois at Urbana-Champaign, Urbana, IL. He also serves as the Director of the Advanced Digital Sciences Center, a large research center of the University of Illinois located in Singapore and funded by Singapore's Agency for Science, Technology, and Research (A*STAR). He received his Ph.D. degree in computer science from the University of California, Berkeley, CA, in 1979. Previously, he had served on the faculty of Purdue University (1979-85), as a Program Director at the National Science Foundation (1988-89), as Fujitsu Visiting Chair Professor of Intelligence Engineering, University of Tokyo (1992), and McKay Visiting Professor of Electrical Engineering and Computer Science, University of California, Berkeley (1994). In 1989, he was awarded a University Scholar of the University of Illinois; in 1998, he received the IEEE Computer Society Technical Achievement Award; in 2000, the IEEE Millennium Medal; in 2003, the Raymond T. Yeh Lifetime Achievement Award from the Society for Design and Process Science; in 2006, the IEEE Computer Society W. Wallace-McDowell Award and the Pan Wen-Yuan Outstanding Research Award, in 2007, the IEEE Computer Society Richard E. Merwin Award and the IEEE-CS Technical Committee on Distributed Processing Outstanding Achievement Award, and in 2009, the IEEE-CS Tsutomu Kanai Award. Wah's current research interests are in the areas of nonlinear search and optimization, multimedia signal processing, and computer networks.
Wah cofounded the IEEE Transactions on Knowledge and Data Engineering in 1988 and served as its Editor-in-Chief between 1993 and 1996, and is the Honorary Editor-in-Chief of Knowledge and Information Systems. He currently serves on the editorial boards of Information Sciences, International Journal on Artificial Intelligence Tools, Journal of VLSI Signal Processing, World Wide Web, and Neural Processing Letters. He had chaired a number of international conferences, including the 2000 IFIP World Congress and the 2006 IEEE/WIC/ACM International Conferences on Data Mining and Intelligent Agent Technology. He has served the IEEE Computer Society in various capacities, including Vice President for Publications (1998 and 1999) and President (2001). He is a Fellow of the AAAS, ACM, and IEEE.
Prof. Edward Tsang - "Intelligent optimization in
finance and economics" 
Edward Tsang is a professor in School of Computer Science and Electronic Engineering at the University of Essex and
the Director of the Centre for Computational Finance and Economic Agents (CCFEA) which he co-founded in 2003.
He has broad research interests in applied artificial intelligence. His research can roughly be grouped into
two overlapping areas: constraint satisfaction and computational finance and economics.
He is a member of IEEE. He actively participates in the Computational Finance and Economics Technical Committee (CFETC),
under the Computational Intelligence Society.
Steering talk: (invited by LION steering committee)
Xin Yao - Evolving and designing neural network ensembles
I am a professor of computer science in the School of Computer Science at the University of Birmingham and the
Director of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA).
I'm also a Fellow of IEEE, a Distinguished Lecturer of the IEEE Computational Intelligence Society, and a
Distinguished Visiting Professor at the Nature Inspired Computation and Applications Laboratory (NICAL)
of University of Science and Technology of China, Hefei, China.
My research interests include evolutionary computation (evolutionary optimization, evolutionary learning,
evolutionary design), neural network ensembles and multiple classifiers (especially on the diversity issue),
meta-heuristic algorithms, data mining, computational complexity of evolutionary algorithms, and various real-world applications.
Tutorials:
Carlos A. Coello Coello - CINVESTAV-IPN - Mexico
Metaheuristics for Multiobjective Optimization
(2 hours)
Abstract
This tutorial provides with a general picture of the
current state-of-the-art in multiobjective optimization
using metaheuristics. First, some historical background
is provided, dating back to the origins of multiobjective
optimization in general. This discussion motivates the
use of metaheuristics for solving multiobjective problems
and includes a brief description of some of the earliest
approaches proposed in the literature. Then, a discussion
on different heuristics used for multiobjective optimization
is provided. This discussion includes evolutionary algorithms,
simulated annealing, tabu search, scatter search, the ant
system, particle swarm optimization and artificial immune
systems. The tutorial finishes with a discussion of
some of the research topics that seem more promising
for the next few years.
A Short Biography of the Speaker
Carlos Artemio Coello Coello received a BSc in Civil
Engineering from the Universidad Autonoma de Chiapas
in Mexico in 1991 (graduating Summa Cum Laude). Then,
he was awarded a scholarship from the Mexican
government to pursue graduate studies in Computer
Science at Tulane University (in the USA). He received
a MSc and a PhD in Computer Science in 1993 and 1996,
respectively. His PhD thesis was one of the first in
the field now called "evolutionary multiobjective
optimization".
Dr. Coello has been a Senior Research Fellow in the
Plymouth Engineering Design Centre (in England) and
a Visiting Professor at DePauw University (in the USA).
He is currently full professor at CINVESTAV-IPN in
Mexico City, Mexico.
He has published over 250 papers in international
peer-reviewed journals and conferences. He has also
co-authored the book "Evolutionary Algorithms for
Solving Multi-Objective Problems" which is now in
its second edition (Springer, New York, 2007)
and has co-edited the book "Applications
of Multi-Objective Evolutionary Algorithms (World
Scientific, 2004).
He has delivered invited talks, keynote speeches and
tutorials at international conferences held in Spain,
USA, Canada, Switzerland, UK, Chile, Colombia, Brazil,
Argentina, India, Italy, China and Mexico.
Dr. Coello has served as a technical reviewer for over
60 international journals and for more than 100
international conferences and actually serves as
associate editor of the journals "IEEE Transactions
on Evolutionary Computation", "Evolutionary Computation",
"Journal of Heuristics", "Soft Computing", "Pattern
Analysis and Applications" and "Computational Optimization
and Applications", and as a member of the
editorial boards of the journals "Engineering
Optimization", and the "International Journal of
Computational Intelligence Research".
He also chairs the "Working Group on Multi-Objective
Evolutionary Algorithms" of the IEEE Computational
Intelligence Society.
He is member of the Mexican Academy of Science, the
Association for Computing Machinery, a Senior Member
of the IEEE, and member of Sigma Xi, The Scientific
Research Society.
He received the 2007 National Research Award from
the Mexican Academy of Science in the area of exact
sciences.
His work currently reports over 4,500 citations.
His current research interests are: evolutionary
multiobjective optimization and constraint-handling
techniques for evolutionary algorithms.
Yaochu Jin, University of Surrey, UK
A Systems Approach to Evolutionary Aerodynamic Design Optimization
(1.5 hours)
Abstract
Evolutionary algorithms (EAs) have shown to be effective in
solving a wide range of test problems. However, it is not straightforward
to apply EAs to complex real-world problems. This tutorial presents a
systems approach to address a few major challenges we face in applying EAs
to complex structural optimization, including the involvement of
time-consuming and multi-disciplinary quality evaluation processes,
changing environments, vagueness in formulating criteria formulation, and
the involvement of multiple sub-systems. Approaches to addressing the
above-mentioned issues with respect to geometry representation, genetic
variations, surrogate model management, robustness and scalability are
discussed in detail.
A Short Biography of the Speaker
Yaochu Jin received the B.Sc., M.Sc., and Ph.D. degrees from Zhejiang
University, Hangzhou, China the Dr.-Ing. degree from Ruhr University Bochum,
Germany. He is currently a Professor of Computational Intelligence and Head
of the Nature-Inspired Computing and Engineering (NICE) Group, Department of
Computing, University of Surrey, UK. Before joining Surrey, he had been a
Principal Scientist and Project Leader with the Honda Research Institute
Europe in Germany since 2003. His research interests include computational
approaches to understanding evolution, learning and development in biology,
and biological approaches to solving complex engineering problems. He has
(co)edited four books and three conference proceedings, authored a monograph,
and (co)authored over 100 peer-reviewed journal and conference papers. Dr.
Jin an Associate Editor of BioSystems, the IEEE Transactions on Neural
Networks, the IEEE Transactions on Systems, Man, and Cybernetics, Part C:
Applications and Reviews, and the IEEE Computational Intelligence Magazine.
He was a past Associate Editor of the IEEE Transactions on Control Systems
Technology, and is currently an editorial member of Soft Computing, Memetic
Computing and Swarm Intelligence Research. Dr. Jin has given plenary / keynote
talks on international conferences on various topics, including
morphogenetic robotics, analysis and synthesis of gene regulatory networks,
evolutionary aerodynamic design optimization and multi-objective machine
learning. He is a Senior Member of IEEE.
Silvia Poles, EnginSoft, Italy
Multiobjective Optimization for Innovation in
Engineering Design
(1.5 hours)
Abstract
ompanies daily need to optimize their products,
hence optimization plays a significant role in
today's design cycle. Problems related to one or more than one objective, originate in several disciplines;
typically using a single optimization technology is
not sufficient to deal with real-life problems,
particularly when the design concerns complex and
expensive products. Therefore, engineers are frequently
asked to solve problems with several conflicting
objective functions. The multiobjective optimization
approach provides a set of non-dominant designs
(Pareto optimality) where a further improvement for one objective is at the expense of all the others:
this allows designers to choose the best solution
for each scenario.
Solving real-world multiobjective problems is not
simple, engineers must address problems connected
to the non-linearity of the functions, complexity of the physics and the computational cost that snowballs
as the number of parameters increases. Moreover, the
coupling between disciplines for design a product can be really challenging, involving several
complicating factors, such as the limitation on the
computational resources, and even a lack of communication
between different departments.
This tutorial is a survey on methodologies to approach
design optimization process, a set of best practices
intended for rapid delivery of high-quality products,
with a specific focus on the numerical algorithms and post-processing used for selecting optimal design
configurations.
A Short Biography of the Speaker
Silvia Poles is an Optimization Consultant at EnginSoft
SpA in Padua (Italian: Padova), Italy. She completed
her MSc degree in Mathematics at University of Padova
in 1996 and subsequently a biennial master in Modeling
and Simulation of Complex Realities at the International
School for Advanced Studies (SISSA) in Trieste, Italy.
Silvia attended workshops on modeling in life and
material sciences and the School on Ecological Economics
at ICTP, Trieste.
Silvia has published several papers on multi-objective
optimizations, performances of noisy optimization problems
and design optimization.
She has also served as a technical reviewer for journals
and international conferences.
Her current research interests are in the fields of
multi-objective optimization and industrial design
optimization, multivariate approximation methodologies,
Multi-Criteria Decision Making (MCDM).
Roberto Battiti, University of Trento, Italy
Reactive Business Intelligence and Data Mining
(2 hours)
Abstract
Humans are innately visual creatures: a big portion of our brains
is devoted to processing visual information. Our ancestors needed
to be very fast to identify predators in the jungle. We need to be
very fast to transform huge amounts of information into insight,
knowledge, engineering designs, choices, decisions.
Visual analytics deals with analytical reasoning facilitated by
interactive visual interfaces. By the term Reactive Business
Intelligence we mean the integration of interactive visual
representations into the discovery and problem solving context.
The search and choice task often involve a learning path between
two entities: the decision maker and the supporting software system.
The decision maker analyses some representative solutions, learning
about concrete possibilities and updating his objectives.
The software system memorizes the user preferences and modifies the
internal search procedure by shifting the focus of attention onto
regions of the design/solution space which are deemed more relevant
by the final user. The integration of automated machine learning
and optimization represents the core principle of Reactive Search
Optimization. The tutorial is focussed onto extending these priciples
in the area of interactive visual analytics.
A Short Biography of the Speaker
Prof. Roberto Battiti received the Laurea degree in Physics from the
University of Trento, Italy, in 1985 and the Ph.D. degree from the California
Institute of Technology (Caltech), USA, in 1990. He is now full professor
of Computer Science at Trento university, deputy director of the DISI
Department (Electrical Engineering and Computer Science) and director
of the LION lab at (machine Learning and Intelligent OptimizatioN). His main
research interests are heuristic algorithms for optimization problems, in
particular reactive search optimization algorithms for discrete optimization
problems. R. Battiti is a fellow of the IEEE. Full details about interests,
research activities and scientific production can be found in the web:
http://lion.dit.unitn.it/~battiti/ , http://reactive-search.org/.
Reactive Business Intelligence.From Data to Models to Insight. R. Battiti and Mauro Brunato.