Bayesian optimisation is a relatively new optimisation paradigm closely linked to machine learning. It is particularly useful for simulation-based optimisation where the evaluation of a solution is computationally very expensive and thus only few solutions can be evaluated.
This talk gives a short introduction to Bayesian optimisation, and then discusses three extensions for particular applications:
The Optimisation Working Group has formed an online Community to help disseminate best practice and encourage the adoption of optimisation methods and technology. More information can be found on the Optimisation Community webpage.
The Optimisation Community is only accessible to NAFEMS members and no significant knowledge or expertise is required to participate. The only requirement is a desire to learn more and to interact with other engineers and scientists who have an interest in expanding their capabilities in the optimisation technical area.
This webinar is available for free to the engineering analysis community, as part of NAFEMS' efforts to bring the community together online.
Professor Juergen Branke, University of Warwick
Juergen Branke is Professor of Operational Research and Systems at Warwick Business School, University of Warwick (UK). His main research interests include metaheuristics and Bayesian optimisation applied to problems under uncertainty, such as simulation optimisation, dynamically changing problems, and multi-objective problems.
Prof. Branke has published over 180 papers in international peer-reviewed journals and conferences. He is Editor of ACM Transactions on Evolutionary Learning and Optimization, Area Editor of the Journal of Heuristics and the Journal on Multi-Criteria Decision Analysis, as well as Associate Editor of IEEE Transactions on Evolutionary Computation and the Evolutionary Computation Journal.