A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

Yu Lei,Qing Cai,Yi Zuo,Maoguo Gong,Licheng Jiao,Wei Li

doi:10.1155/2014/867645

Abstract

A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

Highlights

Timetabling problem has great significance in our daily life, which has drawn more and more attention since 1960s
Examination timetabling problem (ETP), which can be described as the allocation of various examinations into predefined timeslots, is one of the common educational timetabling problems
We propose a double evolutionary pool memetic algorithm for examination timetabling problem, which is denoted as DEPMA

Summary

Introduction

Timetabling problem has great significance in our daily life, which has drawn more and more attention since 1960s. In 1994, Corne et al [10] provided a brief survey on using genetic algorithms to solve general educational timetabling problems, and some issues and future prospects were discussed in their research. More researchers paid their attention to introduce some local search operators to improve the quality of solutions obtained in the examination timetabling problems, which had made remarkable achievements. Alinia Ahandani et al [40] proposed a method for solving examination timetabling problems mainly based on swarm optimization and two-phase hill-climbing local search operators in 2012, which had obtained good results. The main evolutionary pool with a large scale of population is aiming to make the constructed examination timetables more and more feasible, in which there is one local search operator based on reordering strategy.

Examination Timetabling Problems Formulation

Proposed Double Evolutionary Pools

Experimental Study

Findings

Conclusions and Future Work