标题： 基于多项式模型的黑箱目标优化 显示英文标题

标题： Polynomial-Model-Based Optimization for Blackbox Objectives

摘要： 对于广泛的应用，如神经网络或复杂模拟的系统结构是未知的，近似成本高昂甚至不可能。黑盒优化旨在找到这些系统的最优（超）参数，以使预定义的目标函数最小化。多项式模型基础优化（PMBO）是一种新的黑盒优化器，通过将多项式代理模型拟合到目标函数来找到最小值。受贝叶斯优化的启发，该模型根据期望改进的获取函数进行迭代更新，从而平衡利用和探索率，并提供模型的不确定性估计。PMBO针对一组给定的人工和解析函数与其他最先进的算法进行了基准测试。PMBO成功地与这些算法竞争，并在某些情况下甚至超过了所有它们。根据结果，我们认为PMBO是解决广泛学科中出现的黑盒优化任务的关键选择。 显示英文摘要

摘要： For a wide range of applications the structure of systems like Neural Networks or complex simulations, is unknown and approximation is costly or even impossible. Black-box optimization seeks to find optimal (hyper-) parameters for these systems such that a pre-defined objective function is minimized. Polynomial-Model-Based Optimization (PMBO) is a novel blackbox optimizer that finds the minimum by fitting a polynomial surrogate to the objective function. Motivated by Bayesian optimization the model is iteratively updated according to the acquisition function Expected Improvement, thus balancing the exploitation and exploration rate and providing an uncertainty estimate of the model. PMBO is benchmarked against other state-of-the-art algorithms for a given set of artificial, analytical functions. PMBO competes successfully with those algorithms and even outperforms all of them in some cases. As the results suggest, we believe PMBO is the pivotal choice for solving blackbox optimization tasks occurring in a wide range of disciplines.