进化中的差异基因调控网络
Evolving Differentiable Gene Regulatory Networks
摘要：
在过去的二十年里，人工基因调控网络（GRNs）已经显示出它们在解决现实世界中的问题，如代理控制，信号处理和人工生命实验等领域的能力。他们还受益于新的进化方法和动态改进，提高了优化效率。在本文中，我们提出了一个额外的步骤，他们的可用性在机器学习应用程序。我们详细介绍了一个基于GPU的可微GRNs的实现，允许本地优化的GRN架构与随机梯度下降（SGD）。使用一个标准的机器学习数据集，我们评估的方式，进化和SGD可以结合起来，以进一步GRN优化。我们将这些方法与SGD训练的神经网络模型和支持向量机进行比较。
英文摘要：
Over the past twenty years, artificial Gene Regulatory Networks (GRNs) have shown their capacity to solve real-world problems in various domains such as agent control, signal processing and artificial life experiments. They have also benefited from new evolutionary approaches and improvements to dynamic which have increased their optimization efficiency. In this paper, we present an additional step toward their usability in machine learning applications. We detail an GPU-based implementation of differentiable GRNs, allowing for local optimization of GRN architectures with stochastic gradient descent (SGD). Using a standard machine learning dataset, we evaluate the ways in which evolution and SGD can be combined to further GRN optimization. We compare these approaches with neural network models trained by SGD and with support vector machines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
