Details of the talk:
Topic: Approaches in Multi-objective Bayesian Optimisation
Abstract:
Many real-world optimisation problems involve multiple conflicting objectives to be achieved. In some cases, e.g., engineering applications, the objective functions rely on computationally expensive evaluations. Such problems are usually black-box optimisation problems without any closed form for the objective functions. Bayesian optimisation (BO) can be used to alleviate the computational cost and find an approximate set of optimal solutions in minimal function evaluations. These methods rely on a Bayesian model as the surrogate (or metamodel) of the objective functions and find promising decision vectors by optimising an acquisition function. This talk will provide an overview of different methodologies in multi-objective Bayesian optimisation. Those methodologies will be classified into two commonly used approaches: Mono-surrogate and Multi-surrogate. The talk will also cover utilising elements of evolutionary algorithms in BO in the context of mono and multi-surrogate approaches. The talk will briefly cover some real-world problems solved with multi-objective BO.
