Copyrights to these papers may be held by the publishers. The download files are preprints. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.
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)
Bibtex
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.
Keywords: Search/Learning for optimizationMore information: