论文标题
信息理论多目标贝叶斯优化具有连续近似
Information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations
论文作者
论文摘要
许多现实世界应用程序涉及使用连续函数近似值的多个目标优化，这些函数近似值和评估的资源成本进行了权衡。例如，在火箭启动研究中，我们需要使用连续的效率模拟器（例如，将耐受性参数变化到权衡模拟时间和准确性）进行设计评估的设计。目的是通过最大程度地降低评估成本来近似设定的最佳帕累托。在本文中，我们提出了一种新的方法，称为信息理论多目标贝叶斯优化，并连续近似（imoca）}解决此问题。关键想法是为多个目标选择输入和函数近似的顺序，以最大程度地提高最佳帕累托前部信息的信息增益。我们对各种合成和现实基准测试的实验表明，IMOCA在现有的单性方法方面有显着改善。
Many real-world applications involve black-box optimization of multiple objectives using continuous function approximations that trade-off accuracy and resource cost of evaluation. For example, in rocket launching research, we need to find designs that trade-off return-time and angular distance using continuous-fidelity simulators (e.g., varying tolerance parameter to trade-off simulation time and accuracy) for design evaluations. The goal is to approximate the optimal Pareto set by minimizing the cost for evaluations. In this paper, we propose a novel approach referred to as information-Theoretic Multi-Objective Bayesian Optimization with Continuous Approximations (iMOCA)} to solve this problem. The key idea is to select the sequence of input and function approximations for multiple objectives which maximize the information gain per unit cost for the optimal Pareto front. Our experiments on diverse synthetic and real-world benchmarks show that iMOCA significantly improves over existing single-fidelity methods.
