Examination timetabling using scatter search hyper-heuristic

Abstract

Hyper-heuristic can be defined as a ldquoheuristics to choose heuristicsrdquo that intends to increase the level of generality in which optimization methodologies can operate. In this work, we propose a scatter search based hyper-heuristic (SS-HH) approach for solving examination timetabling problems. The scatter search operates at high level of abstraction which intelligently evolves a sequence of low level heuristics to use for a given problem. Each low level heuristic represents a single neighborhood structure. We test our proposed approach on the un-capacitated Carter benchmarks datasets. Experimental results show the proposed SS-HH is capable of producing good quality solutions which are comparable to other hyper-heuristics approaches (with regarding to Carter benchmark datasets).