Kalyanmoy Deb Indian Institute of Technology Kanpur Practical Optimization Using Evolutionary Methods
Bio Kalyanmoy Deb, is a Professor of Mechanical Engineering (Indian Institute Of Technology Kanpur) and the Director of the Kanpur Genetic Algorithms Laboratory (KanGAL) which he established in 1997. Prof. Deb received his Bachelor's degree from IIT Kharagpur (Mechanical Engg 1985). Before joining Alabama, Prof. Deb served with Engineers India Limited (New Delhi) between 1985 and 1987. He was also a Visiting Research Assistant Professor in the Department of General Engineering at the University of Illinois, Urbana Champaign between 1991 and 1992 and worked at Illinois Genetic Algorithms Laboratory (IlliGAL). Author of more than 150 research papers and two books, his latest book on Evolutionary Multiobjective Optimization Algorithms is the first ever compilation of multiobjective optimization algorithms. Professor Deb has organized several conferences and founder-chaired the First Conference on Evolutionary Multicriterion Optimization (EMO 2001) held at Zurich. His research has a practical bend, because of which many researchers and applicationists refer to his research. His NSGA-II paper from IEEE Trans. on Evolutionary Computation (2000) is judged as the Fast-Breaking Paper in Engineering by ESI Web of Science recently.
Seminar Content Many real-world problem solving tasks, involve posing and solving optimization problems, which are usually non-linear, non-differentiable, multi-dimensional, multi-modal, stochastic, and computationally time-consuming. We discuss a number of such practical problems which are, in essence, optimization problems and review the classical optimization methods to show that they are not adequate in solving such demanding tasks. On the other hand, in the past couple of decades, new yet practical optimization methods, based on natural evolutionary techniques, are increasingly found to be useful in meeting the challenges. These methods are population based, stochastic, and flexible, thereby providing an ideal platform to modify them to suit to solve most optimization problems. The breadth of their application domain and ease and efficiency of their working make evolutionary optimization methods promising for taking up the challenges offered by the vagaries of various practical optimization problems.
Michael Littman Rutgers University Probabilistic Planning and Reinforcement Learning
Seminar Content Through a combination of classic papers and more recent work, the course will explore automated decision making from a computer-science perspective. It will examine efficient algorithms, where they exist, for single agent and multiagent planning as well as approaches to learning near-optimal decisions from experience. Topics will include Markov decision processes, stochastic and repeated games, partially observable Markov decision processes, and reinforcement learning.
Luis Correia Universidade de Lisboa Biologicaly Inspired Algorithms, Artificial Life and Self-Organisation
Seminar Content This course will present an overview of computational models inspired by natural systems and by their processes. These models try to capture interesting properties of natural systems, such as self-organisation and robustness. Not only bio-inspired models are used for software development but also to produce embodied artifacts, which interact with the real world as artificial animals. Themes covered include evolutionary algorithms, artificial immune systems, neural networks, swarm models, behaviour based mobile robots. The course is organised in breadth instead of an in-depth study, in order to relate together all these models. It will be shown that, in all cases self-organisation is an underlying concept always present. The last part of the course will study natural self-organised systems, at different levels. Physical and chemical systems on one end and social systems on the other will be discussed. Finally, a few engineered solutions of self-organised systems will be presented.
Talbi El-Ghazali Université des Sciences et Technologies de Lille Efficient metaheuristics: Application to networking and computational biology
Seminar Content In this talk we will present our roadmap in developing efficient metaheuristics for combinatorial optimization problems. This roadmap is based on the landscape analysis of the problem, the design of hybrid metaheuristics, and their parallel implementation on grid computing platforms. Then, we will assess the performance of the presented approaches on some treated applications such as molecular structure prediction and docking, and network design problems.
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