Kozlov Konstantin, Samsonov Alexander
Department of Computational Biology, State Polytechnical University, St. Petersburg, 195251, Russia,
J Supercomput. 2011 Jan 1;57(2):172-178. doi: 10.1007/s11227-010-0390-6.
The Differential Evolution Entirely Parallel (DEEP) method is applied to the biological data fitting problem. We introduce a new migration scheme, in which the best member of the branch substitutes the oldest member of the next branch that provides a high speed of the algorithm convergence. We analyze the performance and efficiency of the developed algorithm on a test problem of finding the regulatory interactions within the network of gap genes that control the development of early Drosophila embryo. The parameters of a set of nonlinear differential equations are determined by minimizing the total error between the model behavior and experimental observations. The age of the individuum is defined by the number of iterations this individuum survived without changes. We used a ring topology for the network of computational nodes. The computer codes are available upon request.
差分进化完全并行（DEEP）方法被应用于生物数据拟合问题。我们引入了一种新的迁移方案，其中分支中的最佳成员替代下一个分支中最老的成员，这提供了较高的算法收敛速度。我们在一个寻找控制早期果蝇胚胎发育的间隙基因网络内调控相互作用的测试问题上分析了所开发算法的性能和效率。通过最小化模型行为与实验观测之间的总误差来确定一组非线性微分方程的参数。个体的年龄由该个体无变化存活的迭代次数定义。我们对计算节点网络使用了环形拓扑结构。可根据要求提供计算机代码。
