Grammatical Evolution for Neural Network Optimization in the Control System Synthesis Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Kazaryan:2017:PCS,

• author = "D. E. Kazaryan and A. V. Savinkov",
• title = "Grammatical Evolution for Neural Network Optimization in the Control System Synthesis Problem",
• journal = "Procedia Computer Science",
• volume = "103",
• pages = "14--19",
• year = "2017",
• note = "\{XII\} International Symposium Intelligent Systems 2016, \{INTELS\} 2016, 5-7 October 2016, Moscow, Russia",
• ISSN = "1877-0509",
• DOI = "doi:10.1016/j.procs.2017.01.002",
• URL = "http://www.sciencedirect.com/science/article/pii/S1877050917300030",
• abstract = "Grammatical evolution is a perspective branch of the genetic programming. It uses evolutionary algorithm based search engine and Backus - Naur form of domain-specific language grammar specifications to find symbolic expressions. This paper describes an application of this method to the control function synthesis problem. Feed-forward neural network was used as an approximation of the control function, that depends on the object state variables. Two-stage algorithm is presented: grammatical evolution optimizes neural network structure and genetic algorithm tunes weights. Computational experiments were performed on the simple kinematic model of a two-wheel driving mobile robot. Training was performed on a set of initial conditions. Results show that the proposed algorithm is able to successfully synthesize a control function.",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, control system synthesis, artificial neural networks",

}

Genetic Programming entries for David Kazaryan A V Savinkov

Citations