Applying the genetic algorithm in nanotechnology for particle optimal space nano measurement.

Nanotechnology invades all fields of the science to become the focus of attention of the researchers over the entire wild world. This technique allows the controlling of the atom and being able to move it freely and easily within an element or compound. Upon these operations, the properties of materials may change and it is possible to obtain new compounds that cannot be obtained by the available scientific methods. It also focuses on the processes of separating, combining, and reforming materials with a single atom or part. Artificial intelligence applications have played an important role in accelerating the space of nanoscale measurements of elements and various algorithms have been proposed in order to improve that. The neural networks, which are used for image recognition, fuzzy logic which is used for prediction and the Genetic Algorithm (GA) for optimization are the most known one. In addition, GA has the ability to improve a set of solutions in order to obtain the optimal one according to tuning of the specific parameters. This paper aims to propose an algorithm based on the original GA to enable reconfiguration of the particles of an element in order to obtain the largest possible space between the particles or atoms of the elements. The available space within an atomic element or molecule is estimated in nanometers. This gives the opportunity for the possibility of proper exploitation of such spaces in more clear and accurate industries manners according to the properties of the resulting material. [ABSTRACT FROM AUTHOR]