Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A robust simulated annealing based examination timetabling system
Computers and Operations Research
High school weekly timetabling by evolutionary algorithms
Proceedings of the 1999 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Survey of Automated Timetabling
Artificial Intelligence Review
Automated Solution of a Highly Constrained School Timetabling Problem - Preliminary Results
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
Timetabling the Classes of an Entire University with an Evolutionary Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
An Evolutionary Algorithm for Solving the School Time-Tabling Problem
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The teaching space allocation problem with splitting
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
Improved squeaky wheel optimisation for driver scheduling
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Polynomial reduction of time-space scheduling to time scheduling
Discrete Applied Mathematics
A hybrid particle swarm optimization based algorithm for high school timetabling problems
Applied Soft Computing
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The timetabling problem is well known to be an NP-complete combinatorial problem. The problem becomes even more complex when addressed to individual timetables of students. The core of dealing with the problem in this application is a timetable builder based on mixed direct-indirect encoding evolved by a genetic algorithm with a self-adaptation paradigm, where the parameters of the genetic algorithm are optimized during the same evolution cycle as the problem itself. The aim of this paper is to present an encoding for self-adaptation of genetic algorithms that is suitable for timetabling problems. Compared to previous approaches we designed the encoding for self-adaptation of not only one parameter or several ones but for all possible parameters of genetic algorithms at the same time. The proposed self-adaptive genetic algorithm is then applied for solving the real university timetabling problem and compared with a standard genetic algorithm. The main advantage of this approach is that it makes it possible to solve a wide range of timetabling and scheduling problems without setting parameters for each kind of problem in advance. Unlike common timetabling problems, the algorithm was applied to the problem in which each student has an individual timetable, so we also present and discuss the algorithm for optimized enrollment of students that minimizes the number of clashing constraints for students.