scholarly journals University Course Timetabling using Multi-population Genetic Algorithm Guided with Local Search and Fuzzy Logic

2013 ◽  
Vol 11 (10) ◽  
pp. 3043-3050
Author(s):  
Sedigheh Asiyaban ◽  
Zohreh Mousavinasab
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Local Search ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Local Optimum ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling

Problem of courses timetabling is a time consuming and demanding issues in any education environment that they are involved in every semester. The main aim of timetabling problem is the allocation of a number of courses to a limited set of resources such as classrooms, time slots, professors and students so that some predefined hard and soft constraints are satisfied. Furthermore, the available resources are used to the best.    In fact course timetabling is one of optimization problems. It has been proved computational complexity of this problem is NP, so there is no optimal solution for that. Therefore, approximation and heuristic techniques are used to find near optimal solutions. Genetic algorithm for its multidirectional feature has been one of the most widely used approaches in recent years. Hence, in this paper an improved genetics algorithm for timetabling problem has been proposed. In proposed algorithm, the fitness of solutions to satisfy soft constraints due to ambiguous nature of those has been specified using fuzzy logic. Also, local search methods have been applied to avoid the genetic algorithm to be trapped in a local optimum. As well as, the multi-population property is intended to reduce the time to reach the optimum solution.  Evaluation results show that the proposed solutions are able to produce promising results for the university courses timetabling.

scholarly journals Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Ahmad Miftah Fajrin ◽  
Chastine Fatichah
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Local Optimum ◽  
Soft Constraints ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Fitness Value ◽  
Hard And Soft Constraints ◽  
The Right ◽  
Course Timetabling Problem

A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

scholarly journals Solving University Course Timetabling Problem Using Multi-Depth Genetic Algorithm

2020 ◽  
Vol 77 ◽  
pp. 01001
Author(s):  
Alfian Akbar Gozali ◽  
Shigeru Fujimura
Keyword(s):  
Genetic Algorithm ◽  
Individual Student ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
Search Spaces ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course ◽  
The University ◽  
Additional Constraints

The University Course Timetabling Problem (UCTP) is a scheduling problem of assigning teaching event in certain time and room by considering the constraints of university stakeholders such as students, lecturers, and departments. The constraints could be hard (encouraged to be satisfied) or soft (better to be fulfilled). This problem becomes complicated for universities which have an immense number of students and lecturers. Moreover, several universities are implementing student sectioning which is a problem of assigning students to classes of a subject while respecting individual student requests along with additional constraints. Such implementation enables students to choose a set of preference classes first then the system will create a timetable depend on their preferences. Subsequently, student sectioning significantly increases the problem complexity. As a result, the number of search spaces grows hugely multiplied by the expansion of students, other variables, and involvement of their constraints. However, current and generic solvers failed to meet scalability requirement for student sectioning UCTP. In this paper, we introduce the Multi-Depth Genetic Algorithm (MDGA) to solve student sectioning UCTP. MDGA uses the multiple stages of GA computation including multi-level mutation and multi-depth constraint consideration. Our research shows that MDGA could produce a feasible timetable for student sectioning problem and get better results than previous works and current UCTP solver. Furthermore, our experiment also shows that MDGA could compete with other UCTP solvers albeit not the best one for the ITC-2007 benchmark dataset.

scholarly journals The application of Coarse-Grained Parallel Genetic Algorithm with Hadoop in University Intelligent Course-Timetabling System

2015 ◽  
Vol 10 (8) ◽  
pp. 11
Author(s):  
Liping Wu
Keyword(s):  
Genetic Algorithm ◽  
Educational Administration ◽  
Coarse Grained ◽  
Map Reduce ◽  
Parallel Genetic Algorithm ◽  
Course Timetabling ◽  
Timetabling Problem ◽  
University Course Timetabling ◽  
Course Timetabling Problem ◽  
University Course

The university course-timetabling problem is a NP-C problem. The traditional method of arranging course is inefficient, causes a high conflict rate of teacher resource or classroom resource, and is poor satisfaction in students. So it does not meet the requirements of modern university educational administration management. However, parallel genetic algorithm (PGA) not only have the advantages of the traditional genetic algorithm(GA), but also take full advantage of the computing power of parallel computing. It can improve the quality and speed of solving effectively, and have a broad application prospect in solving the problem of university course-timetabling problem. In this paper, based on the cloud computing platform of Hadoop, an improved method of fusing coarse-grained parallel genetic algorithm (CGPGA) and Map/Reduce programming model is deeply researched, and which is used to solve the problem of university intelligent courses arrangement. The simulation experiment results show that, compared with the traditional genetic algorithm, the coarse-grained parallel genetic algorithm not only improves the efficiency of the course arrangement and the success rate of the course, but also reduces the conflict rate of the course. At the same time, this research makes full use of the high parallelism of Map/Reduce to improve the efficiency of the algorithm, and also solves the problem of university scheduling problem more effectively.

A Kind of New Immune Genetic Algorithm and its Application

2011 ◽  
Vol 48-49 ◽  
pp. 25-28
Author(s):  
Wei Jian Ren ◽  
Yuan Jun Qi ◽  
Wei Lv ◽  
Cheng Da Li
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Logistic Map ◽  
Optimal Solution ◽  
Optimization Method ◽  
Local Optimum ◽  
Immune Genetic Algorithm ◽  
Chaos Optimization ◽  
Scale Optimization

According to the phenomenon of falling into local optimum during solving large-scale optimization problems and the shortcomings of poor convergence of Immune Genetic Algorithm, a new kind of probability selection method based on the concentration for the genetic operation is presented. Considering the features of chaos optimization method, such like not requiring the solved problems with continuity or differentiability, which is unlike the conventional method, and also with a solving process within a certain range traverse in order to find the global optimal solution, a kind of Chaos Immune Genetic Algorithm based on Logistic map and Hénon map is proposed. Through the application to TSP problem, the results have showed the superior to other algorithms.

scholarly journals A time-predefined approach to course timetabling

2003 ◽  
Vol 13 (2) ◽  
pp. 139-151 ◽  
Author(s):  
Edmund Burke ◽  
Yuri Bykov ◽  
James Newall ◽  
Sanja Petrovic
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Search Time ◽  
Test Problems ◽  
Solution Quality ◽  
Course Timetabling ◽  
Combinatorial Optimization Problems ◽  
University Course Timetabling ◽  
High Level

A common weakness of local search metaheuristics, such as Simulated Annealing, in solving combinatorial optimization problems, is the necessity of setting a certain number of parameters. This tends to generate a significant increase in the total amount of time required to solve the problem and often requires a high level of experience from the user. This paper is motivated by the goal of overcoming this drawback by employing "parameter-free" techniques in the context of automatically solving course timetabling problems. We employ local search techniques with "straightforward" parameters, i.e. ones that an inexperienced user can easily understand. In particular, we present an extended variant of the "Great Deluge" algorithm, which requires only two parameters (which can be interpreted as search time and an estimation of the required level of solution quality). These parameters affect the performance of the algorithm so that a longer search provides a better result - as long as we can intelligently stop the approach from converging too early. Hence, a user can choose a balance between processing time and the quality of the solution. The proposed method has been tested on a range of university course timetabling problems and the results were evaluated within an International Timetabling Competition. The effectiveness of the proposed technique has been confirmed by a high level of quality of results. These results represented the third overall average rating among 21 participants and the best solutions on 8 of the 23 test problems. .

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