Notice of RetractionImpact of the number of generations on the fitness value and the time required to find the optimal solution in standard GA applications

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Genetic Algorithms (GA) are search and optimization algorithms and as such are used for minimizing or maximizing a given function and if possible finding its most suitable solution. They can be used for finding a solution to problems that are difficult to solve with traditional optimization techniques, including problems that are not well defined or difficult to be mathematically modeled, such as the traveling salesman problem and the 2D packing problem. The data we obtain from the applications that implement the GA are visually presented as a graph from which we can see the progress of the GA´s fitness value minimization over the generations, the smallest fitness value, the generation of its occurrence and the solution itself. Within this visualization we also made an analysis on the impact of the number of generations on the fitness value and the time required for finding the optimal solution.