Weighted graphs and university course timetabling
Computers and Operations Research
Complex scheduling with Potts neural networks
Neural Computation
A constraint-based approach to high-school timetabling problems: a case study
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Highly Constrained Problems: The Time-Table Case
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Some Combinatorial Models for Course Scheduling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Recent Developments in Practical Examination Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Examination Timetabling in British Universities: A Survey
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Initialization strategies and diversity in evolutionary timetabling
Evolutionary Computation
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
Semantic components for timetabling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
Solution approaches to the course timetabling problem
Artificial Intelligence Review
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The drawing up of school timetables is a slow, laborious task, performed by people working on the strength of their knowledge of resources and constraints of a specific institution. This paper begins by presenting the timetabling problems that emerge in the context of educational institutions. This is followed by a description of the basic characteristics of the class/teacher timetabling problem. Timetables are considered feasible provided the so-called hard constraints are respected. However, to obtain high-quality timetabling solutions, other conditions should be satisfied in this case - those of soft constraints - which impose satisfaction of a set of desirable conditions for classes and teachers. A multiobjective genetic algorithm was proposed for this timetabling problem, incorporating two distinct objectives. They concern precisely the minimization of the violations of both types of constraints, hard and soft, while respecting the two competing aspects - teachers and classes. A brief description of the characteristics of a genetic multiobjective metaheuristic is presented, followed by the nondominated sorting genetic algorithm, using a standard fitness-sharing scheme improved with an elitist secondary population. This approach represents each timetabling solution with a matrix-type chromosome and is based on special-purpose genetic operators of crossover and mutation developed to act over a secondary population and a fixed-dimension main population of chromosomes. The paper concludes with a discussion of the favorable results obtained through an application of the algorithm to a real instance taken from a university establishment in Portugal.