Sep 5, 2001 · We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities.1 Here, we use two kinds of populations.
From the results for problems generated by an automatic timetabling problem generator, it is shown that TGA obtains a better solution than the simple GA ...
We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities.1 Here, we use two kinds of populations.
Abstract. We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities.1 Here, we use two kinds of popu- lations.
A Co-Evolving Timeslot/Room Assignment Genetic Algorithm Technique for University Timetabling ; Year of Publication, 2001 ; Authors, Ueda H, Ouchi ...
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PDF | The introduction of modularity into institutions of higher education and increasing student numbers mean that timetables are becoming more complex.
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A software solution based on a genetic algorithm (GA) optimization has been designed for creating a university class timetable and has demonstrated the ...
When creating a class for the timetable, an individual (chromosome) must be accepted, read and assigned information (timeslot, room, professor) to each class.
We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities. Here, we use two kinds of populations. The first population is ...
The authors introduce a generic two-dimensional chromosome representation that can easily fit with different university course timetabling problems (see Fig. 1) ...
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