2012
DOI: 10.5815/ijitcs.2012.06.01
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Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem

Abstract: University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this … Show more

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Cited by 12 publications
(11 citation statements)
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“…Courses Timetabling is a routine activity which occurs in each semester in universities. The problem associated with it includes NP-Hard class which is very difficult to solve using a classical algorithm [14]. A number of Events (courses, lecturers) needs to be allocated to a limited space and time to fulfill various [15].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Courses Timetabling is a routine activity which occurs in each semester in universities. The problem associated with it includes NP-Hard class which is very difficult to solve using a classical algorithm [14]. A number of Events (courses, lecturers) needs to be allocated to a limited space and time to fulfill various [15].…”
Section: Methodsmentioning
confidence: 99%
“…After passing through a mutation process, the offspring formed is included in the population. One of the replacement operations is the Worst Replacement (WR) operation and it has been applied in several studies, especially in scheduling problems [14]. It flows when the offspring is formed, by replacing the individuals in the population with high violation or fitness value.…”
Section: Worse Replacementmentioning
confidence: 99%
“…GAs separate objective function from the related constraints when it is used as search criterion. A detailed study of university course timetabling problem using multi population hybrid GAs [11]. GAs start from the randomly generated strings of the initial population.…”
Section: Introductionmentioning
confidence: 99%
“…Early methods include direct heuristics [3], graph coloring [4,5], integer programming [6][7][8], network flow techniques [9,10], all of which have a limitation on the scale of the problem. With the development of computer technology, especially since the 1990s, an increasing number of intelligent algorithms have been applied to solve the timetabling problem, such as genetic algorithm [11][12][13], simulated annealing algorithm [14], taboo search algorithm [15] and hybrid algorithm [16,17]. All these intelligent algorithms above have been proved to be efficient.…”
Section: Introductionmentioning
confidence: 99%
“…With these advantages, genetic algorithm is ideal for solving the timetabling problem. Relevant studies have been conducted, which have some reference value bus also some deficiencies: (1) Constraints are not comprehensive enough, for example, combining classes are rarely taken into consideration [20,21]; (2) the objective function only depends on violations of the constraints and ignores the fact that different courses are of different importance and different periods have different teaching effectiveness [13,20]. This paper proposes improved approaches aiming at these deficiencies.…”
Section: Introductionmentioning
confidence: 99%