Multi-objective Bayesian optimisation with preferences over objectives

Part of Advances in Neural Information Processing Systems 32 (NeurIPS 2019)

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Majid Abdolshah, Alistair Shilton, Santu Rana, Sunil Gupta, Svetha Venkatesh


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.