scholarly journals A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yu Lei ◽  
Maoguo Gong ◽  
Licheng Jiao ◽  
Wei Li ◽  
Yi Zuo ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Memetic Algorithm ◽  
Population Diversity ◽  
Genetic Operators ◽  
Examination Timetabling ◽  
Timetabling Problem ◽  
Uncapacitated Examination Timetabling Problem

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.

Real-Coded Memetic Algorithms with Crossover Hill-Climbing

2004 ◽  
Vol 12 (3) ◽  
pp. 273-302 ◽  
Author(s):  
Manuel Lozano ◽  
Francisco Herrera ◽  
Natalio Krasnogor ◽  
Daniel Molina
Keyword(s):  
Local Search ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Population Diversity ◽  
Hill Climbing ◽  
Problem Instance ◽  
Genetic Operators ◽  
Local Tuning ◽  
Wide Range ◽  
The One

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

A cellular memetic algorithm for the examination timetabling problem

2018 ◽  
Vol 94 ◽  
pp. 118-138 ◽  
Author(s):  
Nuno Leite ◽  
Carlos M. Fernandes ◽  
Fernando Melício ◽  
Agostinho C. Rosa
Keyword(s):  
Memetic Algorithm ◽  
Examination Timetabling ◽  
Timetabling Problem
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