利用新颖偏置GA对满足约束的图的多样性进行采样
Using novelty-biased GA to sample diversity in graphs satisfying constraints
摘要：
许多复杂系统（无论是人工系统还是自然系统）的网络结构在这些系统的运行中起着重要作用。因此，重点放在准确地描述网络使用网络理论度量。然而，当涉及到生成具有类似属性的网络时，可以控制的可用技术和属性集仍然有限。此外，虽然越来越清楚的是，目前用于控制这种网络的生成的一些度量不是非常规范的，使得网络可能在这些约束内潜在地表现出非常不同的高阶结构，但网络生成算法通常产生相当人为的网络，并且缺乏系统地探索网络解决方案空间的机制。在本文中，我们探讨了潜在的多目标新颖偏置GA提供了一个可行的替代这些算法。我们相信我们的研究结果提供了第一个原则证明：（i）可以使用遗传算法生成满足关键经典图论属性集水平的图，以及（ii）可以在这些约束条件下生成不同的解决方案。该文件只是一个初步的步骤，但是，我们确定进一步发展的关键途径。
英文摘要：
The structure of the network underlying many complex systems, whether artificial or natural, plays a significant role in how these systems operate. As a result, much emphasis has been placed on accurately describing networks using network theoretic metrics. When it comes to generating networks with similar properties, however, the set of available techniques and properties that can be controlled for remains limited. Further, whilst it is becoming clear that some of the metrics currently used to control the generation of such networks are not very prescriptive so that networks could potentially exhibit very different higher-order structure within those constraints, network generating algorithms typically produce fairly contrived networks and lack mechanisms by which to systematically explore the space of network solutions. In this paper, we explore the potential of a multi-objective novelty-biased GA to provide a viable alternative to these algorithms. We believe our results provide the first proof of principle that (i) it is possible to use GAs to generate graphs satisfying set levels of key classical graph theoretic properties and (ii) it is possible to generate diverse solutions within these constraints. The paper is only a preliminary step, however, and we identify key avenues for further development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
