Tutorials will be organised at the conference, and will be free to all conference participants. Call for tutorials is now closed.  For any enquiry about tutorials, please contact the Tutorial Chair, Dr. Cara MacNish (Email: cara@csse.uwa.edu.au).  Following tutorials will be presented at the conference:

Note that tutorials (each lasts about 1 hour 40 minutes) will be presented on Sunday 7 December 2008.

Tutorial 1: Recent Trends in Evolutionary Multi-Objective Optimization, Professor Kalyanmoy Deb, India.

Many real-world search and optimization problems are naturally posed as non-linear programming problems having multiple conflicting objectives. Due to lack of suitable solution techniques, such problems are usually artificially converted into a single-objective problem and solved. The difficulty arises because multi-objective optimization problems give rise to a set of Pareto-optimal solutions, each corresponding to a certain trade-off among the objectives. It then becomes important to find not just one Pareto-optimal solution but as many of them as possible. Classical methods are found to be not efficient because they require repetitive applications to find multiple Pareto-optimal solutions and in some occasions repetitive applications do not guarantee finding distinct Pareto-optimal solutions. The population approach of evolutionary algorithms (EAs) allows an efficient way to find multiple Pareto-optimal solutions simultaneously in a single simulation run.

In this tutorial, we shall contrast the differences in philosophies between classical and evolutionary multi-objective methodologies and provide adequate fundamentals needed to understand and use both methodologies in practice. Particularly, major state-of-the-art evolutionary multi-objective optimization (EMO) methodologies will be discussed in detail in the context of their computer implementations. Thereafter, three main application areas of EMO will be discussed with adequate case studies from practice -- (i) applications showing better decision-making abilities through EMO, (ii) applications exploiting the multitude of trade-off solutions of EMO in extracting useful information in a problem, and (iii) applications showing better problem-solving abilities in various other tasks (such as, reducing bloating, solving single-objective constraint handling, and others).

Clearly, EAs have a niche in solving multi-objective optimization problems compared to classical methods. This is why EMO methodologies are getting a growing attention in the recent past. Since this is a comparatively new field of research, in this tutorial, a number of future challenges in the research and application of multi-objective optimization will also be discussed.

This tutorial is aimed for both novices and users of EMO. Those without any knowledge in EMO will have adequate ideas of the procedures and their importance in computing and problem-solving tasks. Those who have been practicing EMO will also have enough ideas and materials for future research, know state-of-the-art results and techniques, and make a comparative evaluation of their research.

Speaker Bio:

Kalyanmoy Deb is currently a Professor of Mechanical Engineering at Indian Institute of Technology Kanpur, India and is the director of Kanpur Genetic Algorithms Laboratory (KanGAL). He is the recipient of the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences for the year 2005. He has also received the `Thomson Citation Laureate Award' from Thompson Scientific for having highest number of citations in Computer Science during the past ten years in India. He is a fellow of Indian National Academy of Engineering (INAE), Indian National Academy of Sciences, and International Society of Genetic and Evolutionary Computation (ISGEC). He has received Fredrick Wilhelm Bessel Research award from Alexander von Humboldt Foundation in 2003.

His main research interests are in the area of computational optimization, modeling and design, and evolutionary algorithms. He has written two text books on optimization and more than 180 international journal and conference research papers. He has pioneered and a leader in the field of evolutionary multi-objective optimization. He is associate editor of two major international journals and an editorial board member of five major journals. More information about his research can be found from http://www.iitk.ac.in/kangal/deb.htm.

Tutorial 2: Puzzle-Based Learning, Professor Zbigniew Michalewicz, Australia.

This tutorial addresses a gap in the educational curriculum for 1st year students by proposing a new course that aims at getting students to think about how to frame and solve unstructured problems. The idea is to increase the student’s mathematical awareness and problem- solving skills by discussing a variety of puzzles. The tutorial makes an argument that this approach – called Puzzle-Based Learning – is very beneficial for introducing mathematics, critical thinking, and problem-solving skills.

The new course has been approved by the University of Adelaide for Faculty of Engineering, Computer Science, and Mathematics. Many other universities are in the process of introducing such a course. The new textbook, Puzzle-Based Learning: Introduction to Critical Thinking, Mathematics, and Problem Solving, is now available, and supported by the website: www.PuzzleBasedLearning.edu.au. The tutorial will provide all information on the contents of the course, teaching materials, software, assignments, etc. and also will give many examples of this approach.

Speaker Bio:

Zbigniew Michalewicz is Professor in School of Computer Science at the University of Adelaide. He completed his Masters degree at Technical University of Warsaw in 1974 and he received Ph.D. degree from the Institute of Computer Science, Polish Academy of Sciences, in 1981. His last post (before arriving in Australia) was a Professor position at the University of North Carolina, USA, where he lectured from 1987 to 2004. Zbigniew Michalewicz also holds Professor positions at the Institute of Computer Science, Polish Academy of Sciences, the Polish- Japanese Institute of Information Technology, and the State Key Laboratory of Software Engineering of Wuhan University, China. He is also associated with the Structural Complexity Laboratory at Seoul National University, South Korea.

Zbigniew Michalewicz has over 30 years of academic and industry experience, and has special interest in new teaching methodologies. In particular, he has been collecting interesting puzzles for over 40 years, and very often these puzzles are being used in his lectures to illustrate some important points.

Tutorial 3: Evolving and Designing Neural Network Ensembles, Professor Xin Yao, United Kingdom.

This tutorial will discuss the evolution and design of artificial neural network ensembles. Topics covered will include:

  1. Introduction
  2. Evolutionary Learning
  3. Evolutionary Artificial Neural Networks (EANNs)
  4. Negative Correlation Learning
  5. Evolving Ensembles
  6. Constructive Learning of Ensembles
  7. Multi-Objective Approaches to EANNs
  8. Conclusions

Speaker Bio:

Xin Yao is a Professor (Chair) of Computer Science at the University of Birmingham, UK. He obtained his BSc from the University of Science and Technology of China (USTC) in Hefei, China, in 1982, MSc from the North China Institute of Computing Technology in Beijing, China, in 1985, and PhD from USTC in Hefei, China, in 1990.

He was a postdoctoral research fellow at the Australian National University (ANU) in Canberra in 1990-91 and at CSIRO Division of Building, Construction and Engineering in Melbourne in 1991-92. He was a lecturer, senior lecturer and an associate professor at the University College, the University of New South Wales (UNSW), the Australian Defence Force Academy (ADFA) in Canberra in 1992-99. He took up a Chair of Computer Science at the University of Birmingham, UK, on the April Fool's Day in 1999.

Currently he is the Director of CERCIA (the Centre of Excellence for Research in Computational Intelligence and Applications) at the University of Birmingham, UK, a Distinguished Visiting Professor of the University of Science and Technology of China in Hefei, China, and a visiting professor of three other universities. He is an IEEE Fellow and a Distinguished Lecturer of IEEE Computational Intelligence Society. He won the 2001 IEEE Donald G. Fink Prize Paper Award and several other best paper awards. In his spare time, he does the voluntary work as the editor-in-chief of IEEE Transactions on Evolutionary Computation, an associate editor or editorial board member of several other journals, and the editor of the World Scientific book series on "Advances in Natural Computation". He has been invited to give more than 45 invited keynote and plenary speeches at conferences and workshops in 16 different countries. He is a Cheung Kong Scholar (Changjiang Chair Professor) of the Ministry of Education of the People's Republic of China.

His research has been well supported by research councils, government organisations and industry. His major research interests include evolutionary computation, neural network ensembles, and their applications. He has more than 230 refereed publications.

Tutorial 4: Getting Evolution to Solve your Practical Problems, Professor Hussein Abbass, Australia

In this tutorial, I will take the audience into a journey to uncover the secrets for getting evolutionary computation techniques to solve practical problems. I will present guidelines on how to analyse the problem, design and choose a suitable evolutionary computation technique to solve it. Examples will be drawn from a multitude of applications including air traffic management, real-time data mining, robotics, and defence.

Target Audience:

  1.  Practitioners and students wishing to use evolutionary computation to solve real world problems
  2.  Post-graduate students searching for a topic for their post-graduate studies
  3.  Academics wishing to teach evolutionary computation

Speaker Bio:

Hussein Abbass is currently a Professor and Chair of Information Technology at the School of Information Technology and Electrical Engineering, University of New South Wales, the Australian Defence Force Academy in Canberra, Australia. He is the Director of the University Defence and Security Applications Research Centre and the Director of the Artificial Life and Adaptive Robotics Laboratory. He is the Chair of the Australian Computer Society National Committee on Complex Systems, the chair of the IEEE-CIS task force on Artificial Life and Complex Adaptive Systems, and a Chief Investigator on the Australian Research Council (ARC) Centre for Complex Systems (ACCS). He holds an Advisory Professor at Vietnam National University, Ho-Chi Minh city, and held visiting positions at Imperial College London and University of Illinois.

He is on the editorial board for two journals IJICC and IJASS. His main research interests include evolutionary games, learning (data mining) and optimization, ensemble learning, and multi-agent systems. He has 170+ refereed publications and his research is funded by the Australian Research Council (ARC), Eurocontrol, and other government organisations and industry.

Tutorial 5: Immunological Computation, Professor Dipankar Dasgupta, USA

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 tutorial will cover the latest advances in Immunological approaches and a few real-world applications.

Speaker Bio:

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 135 papers in book chapters, journals, and international conferences. He 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. His coauthored textbook on Immunological Computation will be published in September 2008. 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. His research lab regularly updates AIS Bibliography and publishes on the web (available at: http://issrl.cs.memphis.edu/AIS/ais_bibliography.pdf).