Inference of compact nonlinear dynamic models by epigenetic local search
Inference of compact nonlinear dynamic models by epigenetic local search
复制标题
DOI：
10.1016/j.engappai.2016.07.004
复制
发表时间：
2016-10-01
影响因子：
8
通讯作者：
Spector, Lee
中科院分区：
文献类型：
--
作者：
La Cava, William;Danai, Kourosh;Spector, Lee
提出了一种利用遗传规划（GP）增强从观测数据推断有意义的动态模型的方法。该方法结合了一个可遗传的表观遗传层，该层指定活性和非活性基因，以便更有效地对模型结构空间进行局部搜索。我们使用表观遗传学的不同特征定义了几种GP实现，如被动结构、表型可塑性和遗传基因调控。为了测试这些实现，我们使用了数百个数据集，这些数据集是由几个工程领域的非线性常微分方程（ODE）和随机构建的非线性ODE模型生成的。结果表明，表观遗传爬坡能产生更紧凑的动态方程，具有更好的适应度值，并能更频繁地识别系统的精确解，验证了表观遗传局部搜索对遗传算法的分类改进。结果进一步表明，当面对复杂的动力学时，表观遗传爬坡减少了推断正确的潜在动力学所需的计算量。然后，我们将该方法应用于三个现实世界系统的识别：级联罐系统，化学蒸馏塔和工业风力涡轮机。在准确性和可理解性方面，我们分析了它的解决方案与理论和黑盒方法的比较。最后，通过种群同源性分析来评价该方法的有效性。结果表明，通过保持程序沉默部分的多样性，表观遗传实现提供了防止过早收敛的保护。(C) 2016 Elsevier Ltd.版权所有。
We introduce a method to enhance the inference of meaningful dynamic models from observational data by genetic programming (GP). This method incorporates an inheritable epigenetic layer that specifies active and inactive genes for a more effective local search of the model structure space. We define several GP implementations using different features of epigenetics, such as passive structure, phenotypic plasticity, and inheritable gene regulation. To test these implementations, we use hundreds of data sets generated from nonlinear ordinary differential equations (ODES) in several fields of engineering and from randomly constructed nonlinear ODE models. The results indicate that epigenetic hill climbing consistently produces more compact dynamic equations with better fitness values, and that it identifies the exact solution of the system more often, validating the categorical improvement of GP by epigenetic local search. The results further indicate that when faced with complex dynamics, epigenetic hill climbing reduces the computational effort required to infer the correct underlying dynamics. We then apply the method to the identification of three real-world systems: a cascaded tanks system, a chemical distillation tower, and an industrial wind turbine. We analyze its solutions in comparison to theoretical and black box approaches in terms of accuracy and intelligibility. Finally, we analyze population homology to evaluate the efficiency of the method. The results indicate that the epigenetic implementations provide protection from premature convergence by maintaining diversity in silenced portions of programs. (C) 2016 Elsevier Ltd. All rights reserved.