Multi-objective Bayesian optimisation with preferences over objectives
We present a multi-objective Bayesian optimisation algorithm that allows the user to express preference-order constraints on the objectives of the type "objective A is more important than objective B". These preferences are defined based on the stability of the obtained solutions with respect to preferred objective functions. Rather than attempting to find a representative subset of the complete Pareto front, our algorithm selects those Pareto-optimal points that satisfy these constraints. We formulate a new acquisition function based on expected improvement in dominated hypervolume (EHI) to ensure that the subset of Pareto front satisfying the constraints is thoroughly explored. The hypervolume calculation is weighted by the probability of a point satisfying the constraints from a gradient Gaussian Process model. We demonstrate our algorithm on both synthetic and real-world problems.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimisationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
R-MBO: A Multi-surrogate Approach for Preference Incorporation in Multi-objective Bayesian Optimisation
Many real-world multi-objective optimisation problems rely on computationally expensive function evaluations. Multi-objective Bayesian optimisation (BO) can be used to alleviate the computation time to find an approximat…
Bayesian OptimisationWind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation
Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind fa…
Bayesian OptimisationCost-aware Multi-objective Bayesian optimisation
The notion of expense in Bayesian optimisation generally refers to the uniformly expensive cost of function evaluations over the whole search space. However, in some scenarios, the cost of evaluation for black-box object…
Bayesian OptimisationBatch Bayesian Optimization via Particle Gradient Flows
Bayesian Optimisation (BO) methods seek to find global optima of objective functions which are only available as a black-box or are expensive to evaluate. Such methods construct a surrogate model for the objective functi…
Bayesian InferenceBayesian OptimisationBayesian OptimizationChoice functions based multi-objective Bayesian optimisation
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A,B,C among this set of five op…
Bayesian Optimisation