<p>Each line represents a different set of parameters. The optimization algorithm has successfully converged to the solution for different sets of parameters. The model is not very sensitive to small changes in the values of some parameters.</p
Abstract Macroscopic models are useful for example in process control and optimization. They are bas...
A simple but reliable model tuning method was developed in order to tune a flight model for a high-f...
In this article, a procedure for characterizing the feasible parameter set of nonlinear models with ...
The concept of parameter-space size adjustment is pn,posed in order to enable successful application...
Abstract: Genetic Algorithm (GA) is a calculus free optimization technique based on principles of na...
The concept of parameter-space size adjustment is proposed in order to enable successful application...
[[abstract]]According to the results of previous research, the genetic algorithm (GA) is a good tech...
The term “appropriate parameters” signifies the correct choice of values has considerable effect on ...
An Evolutionary Genetic Algorithm (EGA) is a simple and generic structure of rules and commands, whi...
Genetic Algorithms (GAs) have been successfully applied to a wide range of engineering optimization ...
Genetic algorithms (GAs) have been used to solve difficult optimization problems in a number of fie...
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and nu...
Abstract — Parameter estimation can also be classified as an optimization where the objective is to ...
In iterative non-linear least-squares fitting, the reliable estimation of initial parameters that le...
This paper describes a new approach for parameter optimization that uses a novel representation for...
Abstract Macroscopic models are useful for example in process control and optimization. They are bas...
A simple but reliable model tuning method was developed in order to tune a flight model for a high-f...
In this article, a procedure for characterizing the feasible parameter set of nonlinear models with ...
The concept of parameter-space size adjustment is pn,posed in order to enable successful application...
Abstract: Genetic Algorithm (GA) is a calculus free optimization technique based on principles of na...
The concept of parameter-space size adjustment is proposed in order to enable successful application...
[[abstract]]According to the results of previous research, the genetic algorithm (GA) is a good tech...
The term “appropriate parameters” signifies the correct choice of values has considerable effect on ...
An Evolutionary Genetic Algorithm (EGA) is a simple and generic structure of rules and commands, whi...
Genetic Algorithms (GAs) have been successfully applied to a wide range of engineering optimization ...
Genetic algorithms (GAs) have been used to solve difficult optimization problems in a number of fie...
In this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and nu...
Abstract — Parameter estimation can also be classified as an optimization where the objective is to ...
In iterative non-linear least-squares fitting, the reliable estimation of initial parameters that le...
This paper describes a new approach for parameter optimization that uses a novel representation for...
Abstract Macroscopic models are useful for example in process control and optimization. They are bas...
A simple but reliable model tuning method was developed in order to tune a flight model for a high-f...
In this article, a procedure for characterizing the feasible parameter set of nonlinear models with ...