当前位置: X-MOL 学术Appl. Netw. Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Learning versus optimal intervention in random Boolean networks
Applied Network Science Pub Date : 2019-12-30 , DOI: 10.1007/s41109-019-0243-z
Matthew R. Karlsen , Sotiris K. Moschoyiannis , Vlad B. Georgiev

Random Boolean Networks (RBNs) are an arguably simple model which can be used to express rather complex behaviour, and have been applied in various domains. RBNs may be controlled using rule-based machine learning, specifically through the use of a learning classifier system (LCS) – an eXtended Classifier System (XCS) can evolve a set of condition-action rules that direct an RBN from any state to a target state (attractor). However, the rules evolved by XCS may not be optimal, in terms of minimising the total cost along the paths used to direct the network from any state to a specified attractor. In this paper, we present an algorithm for uncovering the optimal set of control rules for controlling random Boolean networks. We assign relative costs for interventions and ‘natural’ steps. We then compare the performance of this optimal rule calculator algorithm (ORC) and the XCS variant of learning classifier systems. We find that the rules evolved by XCS are not optimal in terms of total cost. The results provide a benchmark for future improvement.

中文翻译:

随机布尔网络中的学习与最佳干预

随机布尔网络(RBN)是一个可以说很简单的模型,可以用来表达相当复杂的行为,并且已经在各个领域中得到了应用。可以使用基于规则的机器学习来控制RBN,特别是通过使用学习分类器系统(LCS)来控制–扩展分类器系统(XCS)可以演化出一组条件操作规则,这些规则将RBN从任何状态引导到目标状态(吸引子)。但是,就将用于将网络从任何状态引导到指定吸引者的路径上的总成本降至最低而言,由XCS制定的规则可能不是最佳的。在本文中,我们提出了一种算法,用于发现控制随机布尔网络的最佳控制规则集。我们为干预和“自然”步骤分配相对成本。然后,我们比较此最佳规则计算器算法(ORC)和学习分类器系统的XCS变体的性能。我们发现XCS制定的规则在总成本方面并不是最佳的。结果为将来的改进提供了基准。
更新日期:2019-12-30
down
wechat
bug