Surrogate based runtime difference mitigation in asynchronous multi-disciplinary search tasks
Bayesian approaches to the optimisation of complex systems have attracted much research in recent years and have achieved encouraging success.
The project has mainly two aims:
- Develop new training algorithms and new optimisation methods that can deal with very low amount of training data for surrogate models and optimisation evaluations.
- Develop new infill criteria for Bayesian approaches to optimisation which integrate multiple models for estimating different criteria of a multi-objective problem or constraints.