Examining Genetic Algorithm with Guided Search and Self-Adaptive Neighborhood Strategies for Curriculum-Based Course Timetable Problem

Author(s):  
Junrie B. Matias ◽  
Arnel C. Fajardo ◽  
Ruji M. Medina
2021 ◽  
Vol 2 (2) ◽  
pp. 1-13
Author(s):  
Seid Miad Zandavi ◽  
Vera Chung ◽  
Ali Anaissi

The scheduling of multi-user remote laboratories is modeled as a multimodal function for the proposed optimization algorithm. The hybrid optimization algorithm, hybridization of the Nelder-Mead Simplex algorithm, and Non-dominated Sorting Genetic Algorithm (NSGA), named Simplex Non-dominated Sorting Genetic Algorithm (SNSGA), is proposed to optimize the timetable problem for the remote laboratories to coordinate shared access. The proposed algorithm utilizes the Simplex algorithm in terms of exploration and NSGA for sorting local optimum points with consideration of potential areas. SNSGA is applied to difficult nonlinear continuous multimodal functions, and its performance is compared with hybrid Simplex Particle Swarm Optimization, Simplex Genetic Algorithm, and other heuristic algorithms. The results show that SNSGA has a competitive performance to address timetable problems.


SeMA Journal ◽  
2016 ◽  
Vol 73 (3) ◽  
pp. 261-285 ◽  
Author(s):  
Tapan Kumar Singh

2012 ◽  
Vol 12 (6) ◽  
Author(s):  
Wen-Jiang Xiang ◽  
Zhi-Xiong Zhou ◽  
Dong-Yuan Ge ◽  
Qing-Ying Zhang ◽  
Qing-He Yao

2007 ◽  
Vol 177 (20) ◽  
pp. 4295-4313 ◽  
Author(s):  
K.G. Srinivasa ◽  
K.R. Venugopal ◽  
L.M. Patnaik

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