<abstract xmlns="http://www.w3.org/1999/xhtml"><p>This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness from a potential extremum. Results of optimisation experiments demonstrate the greater time-efficiency of F-RCGA in comparison with other RCGAs, as well as the Monte-Carlo method. F-RCGA was validated by using well-known test instances and applied for the optimisation of characteristics of some system dynamics models.</p></abstract>

This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness from a potential extremum. Results of optimisation experiments demonstrate the greater time-efficiency of F-RCGA in comparison with other RCGAs, as well as the Monte-Carlo method. F-RCGA was validated by using well-known test instances and applied for the optimisation of characteristics of some system dynamics models.

Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

Plekhanov Russian University of Economics

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models...

Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

Pubblicato online: 18 giu 2019

Pagine: 87 - 103

Ricevuto: 26 apr 2019

Accettato: 20 mag 2019

DOI: https://doi.org/10.2478/cait-2019-0017

Parole chiave
real-coded genetic algorithm, fuzzy control, system-dynamics, optimisation methods

© 2019 Gayane L. Beklaryan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control

Pubblicato online: 18 giu 2019

Pagine: 87 - 103

Ricevuto: 26 apr 2019

Accettato: 20 mag 2019

DOI: https://doi.org/10.2478/cait-2019-0017

Parole chiavereal-coded genetic algorithm, fuzzy control, system-dynamics, optimisation methods

© 2019 Gayane L. Beklaryan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
real-coded genetic algorithm, fuzzy control, system-dynamics, optimisation methods