A Review on Operation Techniques Using Genetic Algorithm

Abstract

Many algorithms and methods can be used to solve optimization problems. One such is the Genetic Strategy, a straightforward heuristic search algorithm used to identify the solution. Its uses in the real
world are incredibly varied. Recently, genetic algorithms (GAs) have been used as an optimization technique in place of more traditional techniques. They are increasingly in demand as a method of resolving challenging combinatorial optimization issues. One of the most crucial characteristics of this methodology is their capacity to identify optimal functions in cases where standard methods fail.
Selection, crossover, and mutation—the three basic elements of the genetic algorithm—are extensively covered. The procedures used in genetic algorithm problem solution are also used. The extensive range
of applications for the genetic algorithm in the numerous domains of research and all Operation
Research problems is impressive. The Traveling Salesman Problem is the simplest and most straightforward application of genetic algorithms (TSP). The steps in a genetic algorithm are created
by abstracting the issue into its unique nature of its kind, followed by straightforward computations.
They are readily available in various software programmes, including Python, which is quite widely used. In this essay, a general overview of genetic algorithms is presented, along with examples of possible applications.