论文标题
在信息流和连接成本之间具有最佳平衡的神经网络发展
Evolving Neural Networks with Optimal Balance between Information Flow and Connections Cost
论文作者
论文摘要
不断发展的神经网络（NNS）最近将人们的兴趣越来越多，这是一种可能更成功的替代路径。与其他方法相比，它具有许多优势，例如学习NNS的体系结构。但是，极大的搜索空间和许多复杂的相互作用部分的存在仍然是一个主要障碍。最近对许多标准进行了研究，以帮助指导算法并缩小较大的搜索空间。最近，越来越多的研究带来了网络科学的见解，以改善NN的设计。在本文中，我们调查了具有现实世界网络最基本特征之一的Evolvoring NNS架构，即连接成本和信息流之间的最佳平衡。在三个数据集中证明了代表这一平衡的不同指标的性能，并在三个数据集中证明了将更大的选择压力朝向这种平衡的准确性的提高。
Evolving Neural Networks (NNs) has recently seen an increasing interest as an alternative path that might be more successful. It has many advantages compared to other approaches, such as learning the architecture of the NNs. However, the extremely large search space and the existence of many complex interacting parts still represent a major obstacle. Many criteria were recently investigated to help guide the algorithm and to cut down the large search space. Recently there has been growing research bringing insights from network science to improve the design of NNs. In this paper, we investigate evolving NNs architectures that have one of the most fundamental characteristics of real-world networks, namely the optimal balance between connections cost and information flow. The performance of different metrics that represent this balance is evaluated and the improvement in the accuracy of putting more selection pressure toward this balance is demonstrated on three datasets.
