论文标题
可视化多目标优化和多目标的演化历史
Visualising Evolution History in Multi- and Many-Objective Optimisation
论文作者
论文摘要
进化算法被广泛用于解决优化问题。但是，透明度的挑战都在可视化通过问题运行的优化器的过程以及理解来自多个目标问题产生的问题特征的挑战，在这种问题中，很难理解四个或更多的空间维度。这项工作认为人口的可视化是一个优化过程。我们已经将现有的可视化技术调整为多目标问题数据，使用户能够可视化EA流程并确定特定的问题特征，从而提供了对问题景观的更多了解。如果问题景观未知，包含未知的功能或是一个多个目标问题，这一点尤其有价值。我们已经展示了使用此框架如何有效地在一组多个和多目标基准测试问题上使用NSGA-II和NSGA-III优化它们。
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.
