Solving Course Timetabling Problem Using Interrelated Approach

University timetabling is very hectic resources allocation job against tough constraints. The problem is broadly recognized on account of its crucial significance for curriculum activities. Its intensive complexity has challenged the researchers from diverse disciplines for several decades. In the research paper, a novel interrelated approach is employed that primarily depends on Genetic Algorithm supported by Local Search algorithm. Local Search systematizes the events in each timetabling chromosome up to certain degree. Later on GA is likely to obtain more feasible solution available on the search space. The approach has been applied on real dataset and the research direction is validated by promising outcome. The bottom line is minimizing computational time for GA by initializing the set of partial solutions. In addition, exploitation of the resources usage and effective events deployment are key objectives.