First Investigations on Noisy Model-Based Multi-objective Optimization
First Investigations on Noisy Model-Based Multi-objective Optimization
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DOI：
10.1007/978-3-319-54157-0_21
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
Daniel Horn;Melanie Dagge;Xudong Sun;B. Bischl
中科院分区：
文献类型：
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作者：
Daniel Horn;Melanie Dagge;Xudong Sun;B. Bischl
在许多涉及多目标优化的实际应用中，真实的目标函数是不可观测的。相反，只有嘈杂的观察结果可用。近年来，人们对此类噪声在进化多目标优化（EMO）中的影响的兴趣有所增加，并且提出了许多专门的算法。然而，如果目标评估成本昂贵并且只有很少的预算，那么进化算法就不适合。一种流行的解决方案是使用基于模型的多目标优化（MBMO）技术。在本文中，我们对噪声 MBMO 进行了首次研究。为此，我们从 EMO 领域收集了几种噪声处理策略，并将它们应用于 MBMO 算法。我们比较这些策略在两种基准情况下的性能：首先，我们使用同质高斯噪声执行纯人工基准。其次，我们选择机器学习领域的设置，其中潜在噪声的结构未知。
In many real-world applications concerning multi-objective optimization, the true objective functions are not observable. Instead, only noisy observations are available. In recent years, the interest in the effect of such noise in evolutionary multi-objective optimization (EMO) has increased and many specialized algorithms have been proposed. However, evolutionary algorithms are not suitable if the evaluation of the objectives is expensive and only a small budget is available. One popular solution is to use model-based multi-objective optimization (MBMO) techniques. In this paper, we present a first investigation on noisy MBMO. For this purpose we collect several noise handling strategies from the field of EMO and adapt them for MBMO algorithms. We compare the performance of those strategies in two benchmark situations: Firstly, we perform a purely artificial benchmark using homogeneous Gaussian noise. Secondly, we choose a setting from the field of machine learning, where the structure of the underlying noise is unknown.