Solving timetable scheduling problem using genetic algorithms

Sigl, B.; Golub, Marin; Mornar, Vedran

doi:10.1109/iti.2003.1225396

Cited by 32 publications

(21 citation statements)

References 1 publication

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“…Each individual of population (possible solution) represents one time table. Although the approach of genetic algorithm is adopted for solving this problem in which algorithm starts from an infeasible timetable and tries to get the feasible one, but to significantly enhance the performance, modified and better genetic operators should be used [1].…”

Section: Genetic Algorithm Implementation 21 Different Approachesmentioning

confidence: 99%

“…earliness of the class) but other soft constraints are not included. And if the "number of conflicts" includes both hard and soft constraints then it will generate individuals violating the hard constraint as a solution [1].…”

Section: Evaluation Strategymentioning

confidence: 99%

“…Probability of elimination will be more for the individual having larger fitness function value as higher the value of the fitness function lesser the fitness of the individual. There also exists a possibility (though very small) to eliminate the best individual from the population, so there must be a protection mechanism to keep the good genetic material safe and this process is known as elitism [1].…”

Section: Selectionmentioning

confidence: 99%

“…The algorithm generates a random number r from the interval (0, D) and selects an individual which satisfies the condition: max q k <= r [1], [3].…”

Section: Selectionmentioning

confidence: 99%

“…In most of the cases the best choice is to use uniform crossover [1]. It checks all the genes of both the parents.…”

Section: Crossovermentioning

confidence: 99%

See 4 more Smart Citations

Genetically Evolved Solution to Timetable Scheduling Problem

Timilsina¹,

Negi²,

Khurana³

et al. 2015

IJCA

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The simultaneous advancement in genetic modeling and data computational capabilities has prompted profound interest of scientists across the globe in the field of timetable scheduling. The wider usage of timetable scheduling in complex data manipulation and computation has attracted many researchers to put forward their theory regarding the use of genetic algorithms. The progression on this field has increased the efficiency of the timetable to use the limited resources in the given time to get productive results. This paper describes various genetic algorithmic methods.

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Section: Genetic Algorithm Implementation 21 Different Approachesmentioning

confidence: 99%

Section: Evaluation Strategymentioning

confidence: 99%

Section: Selectionmentioning

confidence: 99%

“…The algorithm generates a random number r from the interval (0, D) and selects an individual which satisfies the condition: max q k <= r [1], [3].…”

Section: Selectionmentioning

confidence: 99%

“…In most of the cases the best choice is to use uniform crossover [1]. It checks all the genes of both the parents.…”

Section: Crossovermentioning

confidence: 99%

See 3 more Smart Citations

Genetically Evolved Solution to Timetable Scheduling Problem

Timilsina¹,

Negi²,

Khurana³

et al. 2015

IJCA

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A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling

Jat

Yang

2010

J Sched

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The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimization problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.

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