Novelty search is a powerful tool for finding diverse sets of objects in complicated spaces. Recent experiments on simplified versions of novelty search introduce the idea that novelty search happens at the level of the archive space, rather than individual points. The sparseness measure and archive update criterion create a process that is driven by a two measures: 1) spread out to cover the space while trying to remain as efficiently packed as possible, and 2) metrics inspired by k Nearest Neighbor theory. In this paper, we generalize previous simplifications of novelty search to include traditional population (μ,λ) dynamics for generating new search points, where the population and the archive are updated separately. We provide some theoretical guidance regarding balancing mutation and sparseness criteria and introduce the concept of saturation as a way of talking about fully covered spaces. We show empirically that claims that novelty search is inherently objectiveless are incorrect. We leverage the understanding of novelty search as an optimizer of archive coverage suggest several ways to improve the search, and we demonstrate one simple improvement-generate some new points directly from the archive rather than the parent population.

中文翻译：

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简单查新的初步分析。

新颖性搜索是在复杂空间中查找不同对象集的强大工具。最近关于新颖性搜索简化版本的实验引入了新颖性搜索发生在档案空间级别而不是单个点的想法。稀疏性度量和存档更新标准创建了一个由两个度量驱动的过程：1）展开以覆盖空间，同时尝试保持尽可能有效的打包，2）受 k 最近邻理论启发的度量。在本文中，我们概括了先前新颖性搜索的简化，以包括传统的群体（μ，λ）动态来生成新的搜索点，其中群体和档案分别更新。我们提供了一些关于平衡突变和稀疏标准的理论指导，并引入饱和的概念作为讨论完全覆盖空间的一种方式。我们凭经验证明，新颖性搜索本质上是无客观性的说法是不正确的。我们利用对新颖性搜索的理解作为档案覆盖率的优化器，提出了几种改进搜索的方法，并且我们演示了一个简单的改进 - 直接从档案而不是父群体生成一些新点。