Invited talks and tutorials
Invited talks and tutorials at LION5 have been confirmed by:
Steering talk: (invited by LION steering committee)
Carlos A. Coello Coello - CINVESTAV-IPN - Mexico
Metaheuristics for Multiobjective Optimization
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
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
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
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.