Marcela Zuluaga, Andreas Krause and Markus Püschel (Journal of Machine Learning Research, Vol. 17, No. 104, pp. 1-32, 2016)
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem
Published paper (link to publisher)

In many fields one encounters the challenge of identifying out of a pool of possible designs those that simultaneously optimize multiple objectives. In many applications an exhaustive search for the Pareto-optimal set is infeasible. To address this challenge, we propose the ϵ-Pareto Active Learning (ϵ-PAL) algorithm which adaptively samples the design space to predict a set of Pareto-optimal solutions that cover the true Pareto front of the design space with some granularity regulated by a parameter ϵ. Key features of ϵ-PAL include (1) modeling the objectives as draws from a Gaussian process distribution to capture structure and accommodate noisy evaluation; (2) a method to carefully choose the next design to evaluate to maximize progress; and (3) the ability to control prediction accuracy and sampling cost. We provide theoretical bounds on ϵ-PAL's sampling cost required to achieve a desired accuracy. Further, we perform an experimental evaluation on three real-world data sets that demonstrate ϵ-PAL's effectiveness; in comparison to the state-of-the-art active learning algorithm PAL, ϵ-PAL reduces the amount of computations and the number of samples from the design space required to meet the user's desired level of accuracy. In addition, we show that ϵ-PAL improves significantly over a state-of-the-art multi- objective optimization method, saving in most cases 30% to 70% evaluations to achieve the same accuracy.

Search/Learning for optimization

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