A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems
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Modeling and solution of a complex university course timetabling problem
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This paper describes an Informed Genetic Algorithm (IGA), a genetic algorithm using greedy initialization and directed mutation, to solve a practical university course and student timetabling problem. A greedy method creates some feasible solutions, where all specified hard constraints are not broken, as initial population. A directed mutation scheme is used to reduce violations regarding all given soft constraints and to keep the solutions feasible. Here, IGA creates a timetable in two stages. Firstly, IGA evolves a course timetable using any constraints regarding lecturer, class and room. This stage produce best-sofar timetable. Secondly, using some certain rules IGA evolves the best-sofar timetable using all constraints. The batch student sectioning is done by allowing the first stage timetable to change. Computer simulation to a highly constrained timetabling problem shows that the informed GA is capable of producing a reliable timetable.