Genetic algorithm for graph coloring

Finding solutions to problems in which there is a vast search space can be a task that consumes a lot of time and computational resources. Graph coloring is an example of a complex problem that has many real applications. For this research, was developed and tested a genetic algorithm that in some cases, could be efficient in solving the optimization problem associated with graph coloring, having better results than other genetic algorithms in the literature. To identify the most suitable parameters for the genetic algorithm, this research proposed a methodology where different combinations of parameters were implemented and tested, and the efficiency results of several benchmarks were empirically collected. By identifying the most suitable configurations for the graph coloring problem, we hope to contribute to the most varied research fields and in our daily life problems.