direction finding techniquemutual coupling effectadaptive antenna arraysIn this paper, genetic algorithms are applied to the direction finding of adaptive antenna arrays in the presence of mutual coupling. The
Genetic algorithms cannot entirely automate the process of finding the bestconfiguration. “The genetic algorithm can provide a good starting point,” said Crossley. “But fine tuning or refinement need to be done to obtain the best final solution.” The approach is already used to solve similar ...
In this work, we propose a smart approach that employs genetic algorithms to autonomously optimize the parameters for generating and tailoring stable microcombs. Our scheme controls optical parametric oscillation in a microring resonator to achieve broadband microcombs spanning the entire telecommunication C...
. For the purpose of this paper, genetic algorithms can be divided into two broad categories: simple genetic algorithms (SGAs) and competent genetic algorithms. The term “competent GAs” has been used to describe GAs that solve a large class of optimization problems in scalable manner and will...
Look locally to see what direction to move in (based on the gradient of the function at the current solution) Select the new current solution after deciding how far to move along that path These algorithms work remarkably well for a great majority of practical problems. We’ll revisit this ...
Why is it necessary to optimize the management of strategic projects using genetic algorithms in a Public Organization? It is necessary to optimize the management of planned strategic projects to improve the information security of a public organization because the resources are limited. Generally, the...
Genetic algorithms (GAs) is a well known optimization method [13] that belongs to the general class of evolutionary computation [14], which relies on the premise that in a controlled population the individuals having better traits will finally stand out. Given that in the actuator placement probl...
1.2 Evolutionary algorithms In contrast to gradient-based optimization methods, evolutionary algorithms take inspiration from nature to reach designs that meet a certain design objective. The most well-known and often used examples of evolutionary algorithms are the genetic algorithm (GA) and particle sw...
(3) Accelerated genetic algorithms are able to speed up convergence in each generation iteration and improve the efficiency of finding optimal projections by using genetic operations such as crossover and mutation to complete the optimization process in a limited amount of time. In this paper, an ...
continuous research and exploration of experts, the combination of fractal and intelligent algorithms27,28,29for objective optimization will definitely bring convenience to human production and life. It is foreseeable that this method will become one of the foundations of human research in a hundred ...