Pareto-Weighted-Sum-Tuning: Learning-to-Rank for Pareto Optimization Problems

Abstract

The weighted-sum method is a commonly used technique in Multi-objective optimization to represent different criteria considered in a decision-making and optimization problem. Weights are assigned to different criteria depending on the degree of importance. However, even if decision-makers have an intuitive sense of how important each criteria is, explicitly quantifying and hand-tuning these weights can be difficult. To address this problem, we propose the Pareto-Weighted-Sum-Tuning algorithm as an automated and systematic way of trading-off between different criteria in the weight-tuning process. Pareto-Weighted-Sum-Tuning is a configurable online-learning algorithm that uses sequential discrete choices by a decision-maker on sequential decisions, eliminating the need to score items or weights. We prove that utilizing our online-learning approach is computationally less expensive than batch-learning, where all the data is available in advance. Our experiments show that Pareto-Weighted-Sum-Tuning is able to achieve low relative error with different configurations.