NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this paper we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this paper. Our review paper proposes a new categorization scheme of NEAT's successors into three clusters. NEATbased methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain or 3) based on the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.

中文翻译：

---

“增强拓扑的神经进化”后继者的系统文献综述

神经进化 (NE) 是指使用进化计算 (EC) 算法优化人工神经网络 (ANN) 的一系列方法。增强拓扑的神经进化 (NEAT) 被认为是该领域最有影响力的算法之一。在 NEAT 发明 18 年后，已经提出了大量方法来在不同方面扩展 NEAT。在本文中，我们提出了一个系统的文献综述 (SLR) 来列出和分类 NEAT 之后的方法。我们的审查协议通过合并两个主要电子数据库的发现确定了 232 篇论文。应用确定论文相关性和评估其质量的标准，产生了本文中介绍的 61 种方法。我们的评论论文提出了一种将 NEAT 的后继者分为三个集群的新分类方案。基于 NEAT 的方法的分类基于 1）它们是否考虑特定于搜索空间或适应度领域的问题，2）它们是否结合了来自 NE 和另一个领域的原则，或者 3）基于进化的 ANN 的特定属性。聚类支持研究人员 1) 了解当前最先进的技术，这将使他们能够 2) 探索新的研究方向或 3) 将他们提出的方法与最先进的方法进行基准测试，如果他们有兴趣进行比较，以及 4) 将自己定位在域或 5) 选择最适合其问题的方法。