Offline Multi-Objective Optimization

Understand

Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications.

• In addition to the objective function being black-box and expensive to evaluate, numerous complex real-world problems entail optimizing multiple conflicting objectives, i.e., multi-objective optimization (MOO).
• Nevertheless, offline MOO has not progressed as much as offline single-objective optimization (SOO), mainly due to the lack of benchmarks like Design-Bench for SOO.
• To bridge this gap, we propose a first benchmark for offline MOO, covering a range of problems from synthetic to real-world tasks.