The department of computer science at college of education of pure science held a  scientific symposium entitled “Genetic Algorithms concept and application”

    The department of computer science at college of education of pure science held a  scientific symposium entitled “Genetic Algorithms concept and application” in the main Hall of the conferences and symposiums on Wednesday, 30/11/2016. The aim of the symposium was to give the definition of a genetic algorithm and how it's one of the research methods and find the best solution. It interpolate under the category of the artificial intelligence. It is classified as one of the methods of evolutionary algorithms that rely on the tradition of the work of nature. It has discussed the classification of a genetic algorithm being one of the most important tools of artificial intelligence and intelligent software in general. Algorithm is of the important techniques in the search for the perfect choice for the available set of solutions to a particular design. These genetic technique work to pass the optimum benefits through operations breeding successive, strengthen these qualities, such that these qualities have the biggest ability to enter the breeding process, producing a generation optimizations and repeating  the genetic cyclic will improve the quality gradually. The symposium has addressed the most important applications of genetic algorithm in the fields of bioinformatics, computer engineering, economics, chemistry, manufacturing, mathematics, physics and other fields. It has also clarified the mechanism of genetic algorithm in some details and its basic stages as follows: (i) selection, which is the process of selecting the best individuals on the basis of fitness function. (ii) crossover, which is a generating process of a new generation by mating the best individuals that have been selected. (iii) mutation, which is change some properties of the generation which is output of the hybridization process in order to improve the process. The end of algorithm is evaluate new generation depending on the fitness function and the decision to repeat the above basic operations or to accept the interim results according to the requirements of the solution. It has also discussed some examples of common problems that have been used a genetic algorithm to solve it.

The symposium has confirmed that the selection of genetic algorithm to solve the problems is correlated to the fact that the search space was large or complex, or that can be difficult to use the traditional research methods to resolve these issues. At this time, using  the genetic algorithm will be useful, effective and successful. This will lead to spread using genetic algorithm in many areas.