Int. J. of Bio-Inspired Computation   »   2017 Vol.10, No.4

 

 

Title: An adaptive coevolutionary memetic algorithm for examination timetabling problems

 

Authors: Yu Lei; Maoguo Gong; Licheng Jiao; Jiao Shi; Yu Zhou

 

Addresses:
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi 710071, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi 710071, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi 710071, China
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi 710071, China
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Kowloon, Hong Kong

 

Abstract: In this paper, we present an adaptive coevolutionary memetic algorithm (ACMA) for examination timetabling problems. In our proposed algorithm, the evolutionary search is conducted in two spaces: the heuristic space and the solution space. In the heuristic space, a hyper-heuristic approach is utilised to generate the initial population, and then basic evolutionary operators are used to find the potential heuristic lists. The evolutionary strategy in the heuristic space is regarded as a global search procedure. In the solution space, according to the solution structure, some specific evolutionary operators are designed for expanding the scope of search in solution space. This scheme can be viewed as the local search procedure. By combining two different strategies, the cooperation between them will eventually increase the diversities in the population. In order to determine which space should be selected at each generation, an adaptive parameter is designed based on the proportion of feasible solutions in the current population. Experimental results demonstrated that ACMA obtains competitive results and outperforms the compared approaches on some benchmark instances.

 

Keywords: evolutionary algorithm; memetic algorithm; hyper-heuristic approach; examination timetabling problem.

 

DOI: 10.1504/IJBIC.2015.10004318

 

Int. J. of Bio-Inspired Computation, 2017 Vol.10, No.4, pp.248 - 257

 

Available online: 03 Nov 2017

 

 

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