TIEJ683 Applied Multiobjective Evolutionary Algorithms, 2 op, Insights in to practical evolutionary multiobjective optimization using industrial case studies

In recent times Evolutionary Algorithms based Multiobjective optimization strategies have been utilized in numerous real life problems. The present course will emphasize on many such problems which are either scientific or industrial in nature. The fundamentals of the Evolutionary Multiobjective optimization will be briefly elaborated in the beginning. However, the major part of the course will deal with the strategies for constructing data driven models, techniques for handling noisy or sparse data, identifying the role of variables etc. – all using unconventional Evolutionary Algorithms based Multiobjective approaches propagated by the instructor and his students and collaborators. Such strategies are also used to construct efficient meta-models, when applied in tandem with computationally intensive established modeling techniques like Computational Fluid Dynamics (CFD) or Molecular Dynamics (MD) to reduce the computing burden. Many such practical examples will be discussed and demonstrated.

The course is suitable for master level or post-graduate level studies.