论文标题
基于复杂性的形成和神经进化的基因型表示
Complexity-based speciation and genotype representation for neuroevolution
论文作者
论文摘要
本文介绍了神经进化的一种物种原理，其中，基于隐藏神经元的数量，将不断发展的网络分为物种，这表明搜索空间的复杂性。该物种原理与一种新颖的基因型表示形式无关，其特征在于零基因组冗余，对膨胀的高弹性，重复连接的明确标记以及具有任意拓扑的网络的有效且基于可重复的基于堆栈的评估程序。此外，拟议的物种原则是在几种技术中采用的，旨在促进和保护物种内部和整个生态系统中的多样性。通过实验证明了所提出的框架的竞争性能，称为Cortex。还引入了一个高度可定制的软件平台，该平台实现了本研究中提出的概念，希望它可以作为神经进化领域实验的有用且可靠的工具。
This paper introduces a speciation principle for neuroevolution where evolving networks are grouped into species based on the number of hidden neurons, which is indicative of the complexity of the search space. This speciation principle is indivisibly coupled with a novel genotype representation which is characterised by zero genome redundancy, high resilience to bloat, explicit marking of recurrent connections, as well as an efficient and reproducible stack-based evaluation procedure for networks with arbitrary topology. Furthermore, the proposed speciation principle is employed in several techniques designed to promote and preserve diversity within species and in the ecosystem as a whole. The competitive performance of the proposed framework, named Cortex, is demonstrated through experiments. A highly customisable software platform which implements the concepts proposed in this study is also introduced in the hope that it will serve as a useful and reliable tool for experimentation in the field of neuroevolution.
