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, Guillaume Sergent and Markus Püschel (Proc. International Conference on Machine Learning (ICML), pp. 462-470, 2013)
Active Learning for Multi-Objective Optimization
Preprint (4.7 MB)
Bibtex
In many fields one encounters the challenge of identifying, out of a pool of possible designs, those that simultaneously optimize multiple objectives. This means that usually there is not one optimal design but an entire set of Pareto-optimal ones with optimal trade-offs in the objectives. In many applications, evaluating one design is expensive; thus, an exhaustive search for the Pareto-optimal set is unfeasible. To address this challenge, we propose the Pareto Active Learning (PAL) algorithm which intelligently samples the design space to predict the Pareto-optimal set. Key features of PAL include (1) modeling the objectives as samples from a Gaussian process distribution to capture structure and accomodate 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 PAL’s sampling cost required to achieve a desired accuracy. Further, we show an experimental evaluation on three real-world data sets. The results show PAL’s effectiveness; in particular it improves significantly over a state-of-the-art multi-objective optimization method, saving in many cases about 33% evaluations to achieve the same accuracy.
Keywords: Search/Learning for optimizationMore information: