Development of a Hybrid Tabu Search and Genetic Algorithms for the Examination Timetabling Problem

Genetic Algorithms (GA) and Tabu Search Algorithm (TSA) are amongst the leading research approaches for solving the Examination Timetabling Problem (ETP), however, both algorithms are not optimal.GA returns poor solution, uses excessive memory, experience damage to solution during crossover while solving the ETP.TSA consumes much time, can easily miss some regions of the search space since it uses one solution, and may fail to generate some neighborhood candidate solution.TSA also selects best solution based on the current steps without taking future steps into consideration.This research developed a hybrid of GA and TSA, the GATS algorithm, with the aim of mitigating against the GA's and TS weaknesses to produce higher quality results when solving the ETP.The ETP was modeled as an optimization problem, implemented in Java for the three algorithms and experimented with dataset from Bells University of Technology, Ota.The algorithms' performances were evaluated using first Order Conflict Counts (OCC) and second OCC for students and invigilators respectively, as well as with space complexity.The GA, TSA and GATSA yielded average first Order Conflict Counts (OCC) of 0.0, 0.0 and 0.0 for both students and invigilators.They yielded average second OCC of 5228.5, 18.8 and 0.7 for students and, 0.0, 0.0 and 0.0 for invigilators respectively.The Developed GATSA produced higher quality timetables than TSA and GA, and consumes similar amount of memory as the TSA and has an empirical space complexity of O(n).