Developments in computational models of evolutionary processes have led to the realization of powerful, robust, and general optimization and adaptive systems collectively called evolutionary algorithms. In this paper, we consider one member of this class of algorithms, the genetic algorithm, and describe the features and characteristics that are particularly appropriate for applications in control systems engineering. The versatility and robust qualities of the algorithm are considered and a number of application areas described. Some prospective future directions are also identifie
A problem of structural-parametric control systems identification is considered. The method of genet...
The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this p...
Bibliography: pages 117-120.This thesis report presents the results of a study carried out to determ...
Genetic algorithms (GA's) are global, parallel, stochastic search methods, founded on Darwinian evol...
The use of genetic algorithms and genetic programming in control engineering has started to expand i...
Challenging optimisation problems, which elude acceptable solution via conventional methods, arise r...
Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evol...
Both in the design of dynamical systems, ranging from control systems to state estimators as in the ...
The automatic construction of controllers would be ideal in situations where traditional control the...
Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - al...
Genetic Algorithm is a search heuristic that mimics the process of evaluation. Genetic Algorithms ca...
This paper develops a reusable computing paradigm based on genetic algorithms to transform the "...
SIGLEAvailable from British Library Document Supply Centre-DSC:7769.08577(no 789) / BLDSC - British ...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
The optimisation of nonlinear controller parameters by genetic algorithm (GA) is explored in this pa...
A problem of structural-parametric control systems identification is considered. The method of genet...
The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this p...
Bibliography: pages 117-120.This thesis report presents the results of a study carried out to determ...
Genetic algorithms (GA's) are global, parallel, stochastic search methods, founded on Darwinian evol...
The use of genetic algorithms and genetic programming in control engineering has started to expand i...
Challenging optimisation problems, which elude acceptable solution via conventional methods, arise r...
Genetic algorithms (GA'S) are global, parallel, stochastic search methods, founded on Darwinian evol...
Both in the design of dynamical systems, ranging from control systems to state estimators as in the ...
The automatic construction of controllers would be ideal in situations where traditional control the...
Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - al...
Genetic Algorithm is a search heuristic that mimics the process of evaluation. Genetic Algorithms ca...
This paper develops a reusable computing paradigm based on genetic algorithms to transform the "...
SIGLEAvailable from British Library Document Supply Centre-DSC:7769.08577(no 789) / BLDSC - British ...
In this thesis we investigate how intelligent techniques, such as Evolutionary Algorithms, can be ap...
The optimisation of nonlinear controller parameters by genetic algorithm (GA) is explored in this pa...
A problem of structural-parametric control systems identification is considered. The method of genet...
The optimization of nonlinear controller parameters by Genetic Algorithm (GA) is explored in this p...
Bibliography: pages 117-120.This thesis report presents the results of a study carried out to determ...