Visualising Evolution History in Multi- and Many-Objective Optimisation
Visualising Evolution History in Multi- and Many-Objective Optimisation
复制标题
可视化多目标优化中的进化历史
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
M. J. Craven
中科院分区：
文献类型：
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作者：
Mathew J. Walter;D. Walker;M. J. Craven
进化算法被广泛用于解决优化问题。然而，透明度的挑战出现在两个可视化的优化器通过一个问题的操作过程和理解的问题功能产生的许多客观的问题，理解四个或更多的空间维度是困难的。这项工作考虑了人口的可视化作为一个优化过程执行。我们已经适应了现有的可视化技术，以多目标和多目标的问题数据，使用户能够可视化的EA过程，并确定具体的问题特征，从而提供了一个更好的理解的问题景观。如果问题的前景是未知的，包含未知的功能或是一个多目标的问题，这是特别有价值的。我们已经展示了如何使用这个框架是有效的一套多目标和多目标的基准测试问题，优化他们与NSGA-II和NSGA-III。
Evolutionary algorithms are widely used to solve optimisation problems. However, challenges of transparency arise in both visualising the processes of an optimiser operating through a problem and understanding the problem features produced from many-objective problems, where comprehending four or more spatial dimensions is difficult. This work considers the visualisation of a population as an optimisation process executes. We have adapted an existing visualisation technique to multi- and many-objective problem data, enabling a user to visualise the EA processes and identify specific problem characteristics and thus providing a greater understanding of the problem landscape. This is particularly valuable if the problem landscape is unknown, contains unknown features or is a many-objective problem. We have shown how using this framework is effective on a suite of multi- and many-objective benchmark test problems, optimising them with NSGA-II and NSGA-III.