Abstract
The simultaneous advancement in genetic modeling and data computational capabilities has prompted profound interest of scientists across the globe in the field of timetable scheduling. The wider usage of timetable scheduling in complex data manipulation and computation has attracted many researchers to put forward their theory regarding the use of genetic algorithms. The progression on this field has increased the efficiency of the timetable to use the limited resources in the given time to get productive results. This paper describes various genetic algorithmic methods.
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