Efficient Numerical Optimization Algorithm Based on Genetic Algorithm for Inverse Problem
Efficient Numerical Optimization Algorithm Based on Genetic Algorithm for Inverse Problem
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发表时间：
2000-07
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通讯作者：
D. Tominaga;Nobuto Koga;M. Okamoto
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作者：
D. Tominaga;Nobuto Koga;M. Okamoto
我们已经开发了一种基于遗传算法（GA）的非线性系统模型优化的有效算法。通过实验观察到的一些系统组件的动态响应（时间过程）的系统组件之间的相互作用机制的估计通常被称为“逆问题”。属于幂律形式主义的S-系统是解决此类逆问题的最佳表示之一; S-系统具有足够丰富的结构以捕获所有相关动力学。在本文中，为了解决反问题的目的，我们介绍了遗传算法，并提出了一个有效的程序估计的大量参数的S-系统形式主义。我们将我们的方法应用到一个简单的振荡系统和一个基因表达网络。
We have developed an efficient algorithm based on the Genetic Algorithm(GA) for optimization of a model of a nonlinear system. Estimation of the interaction mechanisms among system components by using experimentally observed dynamic responses (time-courses) of some of the system components is generally referred to as "inverse problem". The S-system, which belongs to power-law formalism, is one of the best representations to solve such an inverse problem; the S-system is rich enough in structure to capture all relevant dynamics. In this paper, for the purpose of solving the inverse problem, we introduce the GA and propose an efficient procedure for the estimation of large numbers of parameters in the S-system formalism. We applied our method to a simple oscillatory system and a gene expression network.