Are we generating instances uniformly at random?
Are we generating instances uniformly at random?
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我们是否均匀随机生成实例？
DOI：
10.1109/cec.2017.7969499
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. A. Lozano
中科院分区：
文献类型：
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作者：
Josu Ceberio;A. Mendiburu;J. A. Lozano
在进化计算中，通常使用一组实例作为测试床来评估和比较新优化算法的性能。在某些情况下，可用现实世界实例，因此它们被用来构成实验基准。不幸的是，这不是一般情况。由于难以获得现实世界实例，或者由于文献中定义的优化问题并不完全像行业中定义的问题，因此从业人员被迫创建人工实例。在本文中，我们研究了与随机生成人工实例有关的一些方面。特别是，我们阐述了这样一个假设，即在参数空间中随机进行随机采样等同于在函数空间中随机采样。通过一些实验说明，我们证明对于某种类型的算法，该假设不存在。
In evolutionary computation, it is common practice to use sets of instances as test-beds for evaluating and comparing the performance of new optimisation algorithms. In some cases, real-world instances are available, and, thus, they are used to constitute the experimental benchmark. Unfortunately, this is not the general case. Due to the difficulties for obtaining real-world instances, or because the optimisation problems defined in the literature are not exactly as those defined in the industry, practitioners are forced to create artificial instances. In this paper, we study some aspects related to the random generation of artificial instances. Particularly, we elaborate on the assumption that states that sampling uniformly at random in the space of parameters is equivalent to sampling uniformly at random in the space of functions. Illustrated with some experiments, we prove that for some type of algorithms this assumption does not hold.