Abstract—In this paper a linear representation of a synthetic genetic regulatory network (GRN) model is derived and it is used for evolving linear dynamic controllers for nonlinear systems. A case study is considered in which running the genetic algorithm on the elements of the system matrix of a linear controller is unable to evolve and reach the control ends, while running the genetic algorithm on the genes of an artificial cell with linear regulatory networks evolves and a linear controller is achieved. This justifies the computational burden imposed on computations due to GRN dynamics as GRN representation increases the evolvability of the controller.
Index Terms—Genetic regulatory networks, linear artificial cells, linear dynamic controller, genetic algorithms, evolvability.
The authors are with the Department of Electrical Engineering, Tarbiat Modares University, Tehran, Iran (e-mail: navidrahbariasr@gmail.com, majd@modares.ac.ir, majid_ee82@yahoo.com, m.behnam@modares.ac.ir).
Cite:Navid Rahbari Asr, Vahid Johari Majd, Majid Hassan Zadeh Shojai, and Mehdi Behnam, "Linear Dynamic Controllers Evolved by Genetic Regulatory Network Based Artificial Cells," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 39-43, 2013.