AI-Based Optimization:

Examples in Product Development and Manufacturing

I use the term “AI-Based Optimization” in a quite generic way to characterize data-driven optimization methods and tasks.  In the first part of the talk, I will focus on the idea to automatically optimize the optimization algorithm, which has been around for quite some time now. For algorithm configuration, I will present some examples illustrating that this task can be handled by direct global optimization algorithms as well. I will give an example of how a combinatorial design space of 4608 configuration variants of evolution strategies can be explored and analyzed using data mining. This approach provides an opportunity for discovering the unexplored areas of the optimization algorithm design space.

In the second part of the presentation, I will discuss a range of real-world applications for which we develop and deploy AI-based solutions in product development and manufacturing. Such applications include, but are not limited to, heuristic optimization algorithms. This part of the talk will be high-level, providing an overview and some lessons learned.

Brief Biography

Thomas Bäck (Fellow, IEEE) received the Diploma degree in Computer Science in 1990 and the PhD degree in Computer Science in 1994, both from the University of Dortmund, Germany. He is a Professor of Computer Science with the Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Netherlands. His research interests include evolutionary computation, machine learning, and real-world applications, especially in sustainable smart industry and health.

Dr. Bäck has been elected as a member of the Royal Netherlands Academy of Arts and Sciences (KNAW, 2021), an IEEE Fellow (class of 2022), and as a member of Academia Europaea (2022). He was a recipient of the IEEE Computational Intelligence Society (CIS) Evolutionary Computation Pioneer Award in 2015, was elected a Fellow of the International Society of Genetic and Evolutionary Computation in 2003, and received the best PhD thesis award from the German Society of Computer Science (GI) in 1995.

He currently serves as an Associate Editor for the IEEE Transactions on Evolutionary Computation and Artificial Intelligence Review journals and area editor for the ACM Transactions on Evolutionary Learning and Optimization. He is an editorial board member of the Natural Computing Journal (Springer), Artificial Intelligence Journal, Journal of Big Data (Springer), and Journal of Intelligent Manufacturing (Springer). He was also co-editor-in-chief of the Handbook of Evolutionary Computation (CRC Press/Taylor & Francis 1997), co-editor of the Handbook of Natural Computing (Springer, 2013), author of Evolutionary Computation in Theory and Practice (OUP, New York, 1996) and co-author of Contemporary Evolution Strategies (Springer, 2013).

WED 31 AUGUST 4 PM – 5 PM AEST MELBOURNE

ZOOM MEETING ID: 873 1557 5255; PASSWORD: 778635