The standard versions of Evolutionary Algorithms (EAs) have two main
drawbacks: unlearned termination criteria and slow convergence.
Although several attempts have been made to modify the original
versions of Evolutionary Algorithms (EAs), only very few of them
have considered the issue of their termination criteria. In general,
EAs are not learned with automatic termination criteria, and they
cannot decide when or where they can terminate. On the other hand,
there are several successful modifications of EAs to overcome their
slow convergence. One of the most effective modifications is Memetic
Algorithms. In this paper, we modify genetic algorithm (GA), as an
example of EAs, with new termination criteria and acceleration
elements. The proposed method is called GA with Automatic
Accelerated Termination (G3AT). In the G3AT method, Gene
Matrix (GM) is constructed to equip the search process with
a self-check to judge how much exploration has been done. Moreover,
a special mutation operation called ``Mutagenesis