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.

中文翻译：

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DEEP——基因调控网络的差异进化完全并行方法

差分进化完全并行 (DEEP) 方法应用于生物数据拟合问题。我们引入了一种新的迁移方案，其中分支中最好的成员替换下一个分支中最老的成员，这提供了算法的高速收敛。我们在一个测试问题上分析了所开发算法的性能和效率，该测试问题是在控制早期果蝇胚胎发育的间隙基因网络中寻找调节相互作用。一组非线性微分方程的参数是通过最小化模型行为和实验观察之间的总误差来确定的。个体的年龄由该个体在没有变化的情况下幸存下来的迭代次数定义。我们为计算节点网络使用了环形拓扑。