Title
DEEP—differential evolution entirely parallel method for gene regulatory networks
Abstract
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.
Year
DOI
Venue
2009
https://doi.org/10.1007/s11227-010-0390-6
The Journal of Supercomputing
Keywords
Field
DocType
Differential evolution,Optimization,Regulatory gene networks
Biological data,Computer science,Algorithm,Theoretical computer science,Differential evolution,Nonlinear differential equations,Gap gene,Algorithm convergence,Gene regulatory network,Ring network
Conference
Volume
Issue
ISSN
57
2
0920-8542
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
K Kozlov15312.56
Alexander Samsonov2171.98