Abstract：
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 article, 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 article. Our review article proposes a new categorization scheme of NEAT\'s successors into three clusters. NEAT-based 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) 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.
摘要：
NeuroEvolution（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。它发明18年后，已经提出了过多的方法，在不同方面扩展了NEAT。在这篇文章中,我们提出了一个系统的文献综述(SLR)来列出和分类NEAT成功的方法。我们的审查协议通过合并两个主要电子数据库的发现来识别232篇论文。应用确定论文相关性并评估其质量的标准，产生了本文介绍的61种方法。我们的评论文章提出了一种将NEAT的继任者分为三个集群的新分类方案。基于NEAT的方法根据1）是否考虑特定于搜索空间或健身环境的问题进行分类，2)它们是否结合了来自NE和另一个领域的原则，或3)进化人工神经网络的特定特性。聚类支持研究人员1）了解使他们能够实现的最新技术，2）探索新的研究方向或3）将其提出的方法与最新技术进行基准测试，如果他们有兴趣比较，和4）将自己定位在域中或5）选择最适合其问题的方法。
