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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 841748, 16 pages
http://dx.doi.org/10.1155/2015/841748
Research Article

Modifying Regeneration Mutation and Hybridising Clonal Selection for Evolutionary Algorithms Based Timetabling Tool

1Faculty of Industrial Technology, Pibulsongkram Rajabhat University, Phitsanulok 65000, Thailand
2Industrial Engineering Department, Centre of Operations Research and Industrial Applications (CORIA), Faculty of Engineering, Naresuan University, Phitsanulok 65000, Thailand
3Newcastle University Business School, Newcastle University, Newcastle upon Tyne NE1 7RU, UK

Received 19 September 2014; Accepted 16 December 2014

Academic Editor: Yudong Zhang

Copyright © 2015 Thatchai Thepphakorn et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper outlines the development of a new evolutionary algorithms based timetabling (EAT) tool for solving course scheduling problems that include a genetic algorithm (GA) and a memetic algorithm (MA). Reproduction processes may generate infeasible solutions. Previous research has used repair processes that have been applied after a population of chromosomes has been generated. This research developed a new approach which (i) modified the genetic operators to prevent the creation of infeasible solutions before chromosomes were added to the population; (ii) included the clonal selection algorithm (CSA); and the elitist strategy (ES) to improve the quality of the solutions produced. This approach was adopted by both the GA and MA within the EAT. The MA was further modified to include hill climbing local search. The EAT program was tested using 14 benchmark timetabling problems from the literature using a sequential experimental design, which included a fractional factorial screening experiment. Experiments were conducted to (i) test the performance of the proposed modified algorithms; (ii) identify which factors and interactions were statistically significant; (iii) identify appropriate parameters for the GA and MA; and (iv) compare the performance of the various hybrid algorithms. The genetic algorithm with modified genetic operators produced an average improvement of over 50%.