Visualising the global structure of search landscapes: genetic improvement as a case study
Visualising the global structure of search landscapes: genetic improvement as a case study
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可视化搜索景观的全局结构：遗传改良作为案例研究
DOI：
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发表时间：
2018
影响因子：
2.6
通讯作者：
G. Ochoa
中科院分区：
文献类型：
--
作者：
Nadarajen Veerapen;G. Ochoa
搜索景观是描述计算搜索空间结构的常见比喻。可以计算不同的景观度量并用于预测搜索难度。然而，这个隐喻在视觉化方面却有福尔斯不足，因为它很难代表复杂的景观，无论是在大小和维度方面。本文结合了局部最优网络，作为一个紧凑的表示的全局结构的搜索空间，和降维，使用t分布随机邻居嵌入算法，为了既把隐喻的生活，并传达新的见解搜索过程。作为一个案例研究，两个基准程序，根据遗传改进错误修复的情况下，使用所提出的方法进行分析和可视化。局部最优网络的迭代局部搜索和混合遗传算法，在不同的街区，进行了比较，突出了景观的差异是如何探索。
The search landscape is a common metaphor to describe the structure of computational search spaces. Different landscape metrics can be computed and used to predict search difficulty. Yet, the metaphor falls short in visualisation terms because it is hard to represent complex landscapes, both in terms of size and dimensionality. This paper combines local optima networks, as a compact representation of the global structure of a search space, and dimensionality reduction, using the t-distributed stochastic neighbour embedding algorithm, in order to both bring the metaphor to life and convey new insight into the search process. As a case study, two benchmark programs, under a genetic improvement bug-fixing scenario, are analysed and visualised using the proposed method. Local optima networks for both iterated local search and a hybrid genetic algorithm, across different neighbourhoods, are compared, highlighting the differences in how the landscape is explored.
影响因子：
14.3
作者：
Petke J
通讯作者：
Petke J