摘要翻译：
本文用Kansa方法求解生物领域的微分方程组。Kansa方法的一个挑战是在径向基函数中选择形状参数的最优值以达到该方法的最佳效果，因为目前还没有任何可用的解析方法来获得最佳形状参数。为此，我们设计了一种遗传算法来检测一个接近最优的形状参数。实验结果表明，该策略在HIV和流感等生物微分模型系统中是有效的。此外，我们还证明了在遗传策略中使用伪组合公式进行交叉会导致形状参数的近似最优选择收敛。
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英文标题：
《Numerical investigation of Differential Biological-Models via GA-Kansa
  Method Inclusive Genetic Strategy》
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作者：
Kourosh Parand, Mohammad Hemami, Mohammad Kazem Fallah
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最新提交年份：
2017
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Mathematics        数学
二级分类：Classical Analysis and ODEs        经典分析与颂歌
分类描述：Special functions, orthogonal polynomials, harmonic analysis, ODE's, differential relations, calculus of variations, approximations, expansions, asymptotics
特殊函数、正交多项式、调和分析、Ode、微分关系、变分法、逼近、展开、渐近
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英文摘要：
  In this paper, we use Kansa method for solving the system of differential equations in the area of biology. One of the challenges in Kansa method is picking out an optimum value for Shape parameter in Radial Basis Function to achieve the best result of the method because there are not any available analytical approaches for obtaining optimum Shape parameter. For this reason, we design a genetic algorithm to detect a close optimum Shape parameter. The experimental results show that this strategy is efficient in the systems of differential models in biology such as HIV and Influenza. Furthermore, we prove that using Pseudo-Combination formula for crossover in genetic strategy leads to convergence in the nearly best selection of Shape parameter.
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PDF链接：
https://arxiv.org/pdf/1705.09381