@Article{iasc.2020.013915,
AUTHOR = {Abayomi-Alli A., Misra S., Fernández-Sanz L., Abayomi-Alli O., Edun A. R.},
TITLE = {Genetic Algorithm and Tabu Search Memory with Course Sandwiching  (GATS_CS) for University Examination Timetabling},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {3},
PAGES = {385--396},
URL = {http://www.techscience.com/iasc/v26n3/39998},
ISSN = {2326-005X},
ABSTRACT = {University timetable scheduling is a complicated constraint problem because 
educational institutions use timetables to maximize and optimize scarce 
resources, such as time and space. In this paper, an examination timetable 
system using Genetic Algorithm and Tabu Search memory with course 
sandwiching (GAT_CS), was developed for a large public University. The concept 
of Genetic Algorithm with Selection and Evaluation was implemented while the 
memory properties of Tabu Search and course sandwiching replaced Crossover 
and Mutation. The result showed that GAT_CS had hall allocation accuracies of 
96.07% and 99.02%, unallocated score of 3.93% and 0.98% for first and second 
semesters, respectively. It also automatically sandwiched (scheduled) multiple 
examinations into single halls with a simulation time in the range of 20-29.5 
seconds. The GAT_CS outperformed previous related works on the same 
timetable dataset. It could, however, be improved to reduce clashes, 
duplications, multiple examinations and to accommodate more system-defined 
constraints.},
DOI = {10.32604/iasc.2020.013915}
}