Data-Driven Evolutionary Optimization: An Overview and Case Studies
Data-Driven Evolutionary Optimization: An Overview and Case Studies
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DOI：
10.1109/tevc.2018.2869001
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发表时间：
2019-06-01
影响因子：
14.3
通讯作者：
Miettinen, Kaisa
中科院分区：
文献类型：
--
作者：
Jin, Yaochu;Wang, Handing;Miettinen, Kaisa
大多数进化优化算法假设目标和约束函数的评估是简单的。然而，在解决许多现实世界的优化问题时，这样的目标函数可能不存在。相反，计算昂贵的数值模拟或昂贵的物理实验必须进行适应性评估。在更极端的情况下，只有历史数据可用于执行优化，并且在优化期间无法生成新数据。解决由模拟、物理实验、生产过程或日常生活中收集的数据驱动的进化优化问题称为数据驱动的进化优化。在本文中，我们提供了一个不同的数据驱动的进化优化问题的分类，讨论在数据驱动的进化优化的主要挑战方面的性质和数量的数据，以及在优化过程中的新数据的可用性。给出了真实世界的应用实例，以说明不同类别的数据驱动的优化问题的不同模型管理策略。
Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist. Instead, computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this paper, we provide a taxonomy of different data driven evolutionary optimization problems, discuss main challenges in data-driven evolutionary optimization with respect to the nature and amount of data, and the availability of new data during optimization. Real-world application examples are given to illustrate different model management strategies for different categories of data-driven optimization problems.