Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models. However, in many real-world applications, accuracy is only one of multiple performance criteria that must be considered. Optimizing these objectives simultaneously on a complex and diverse search space remains a challenging task. In this paper, we propose MO-DEHB, an effective and flexible multi-objective (MO) optimizer that extends the recent evolutionary Hyperband method DEHB. We validate the performance of MO-DEHB using a comprehensive suite of 15 benchmarks consisting of diverse and challenging MO problems, including HPO, neural architecture search (NAS), and joint NAS and HPO, with objectives including accuracy, latency and algorithmic fairness. A comparative study against state-of-the-art MO optimizers demonstrates that MO-DEHB clearly achieves the best performance across our 15 benchmarks.

翻译：超参数优化（HPO）是一种自动化调整机器学习（ML）模型的强大技术。然而，在许多实际应用中，准确率仅是必须考虑的多个性能指标之一。在复杂且多样化的搜索空间中同时优化这些目标仍然是一项具有挑战性的任务。本文提出MO-DEHB，一种有效且灵活的多目标优化器，它扩展了近期提出的进化超带方法DEHB。我们使用包含15个基准测试的综合套件验证了MO-DEHB的性能，这些基准测试涵盖了多样且具有挑战性的多目标问题，包括HPO、神经架构搜索（NAS）以及联合NAS与HPO，目标涉及准确率、延迟和算法公平性。与最先进的多目标优化器的比较研究表明，MO-DEHB在我们的15个基准测试中明显取得了最佳性能。