Complete lesson planning system (timetable generator) using genetic algorithms in C# and MS SQL
About the project
Make a class schedule is one of these NP-hard problems. The problem can be solved using a heuristic search algorithm to find the optimal solution, but this only works for simple cases. For more complex inputs and requirements, finding a significantly correct answer may take some time or even be impossible. This is where genetic algorithms come into play.
In this article, I assume that you are familiar with the basic concepts of genetic algorithms, and I will not describe them in detail because it has already been done many times. When making a class schedule, you need to consider many requirements (number of teachers, students, classes and classrooms, class size, school laboratory equipment and many others). These requirements can be divided into several groups depending on their importance. Difficult conditions (if you break one, then the schedule is unworkable):
- A class can only be placed in a free classroom.
- No professor or group of students may have more than one class at a time.
- A classroom should have enough seats to accommodate all students.
- To place a class in a classroom, the classroom must have laboratory equipment (computers, in our case) if the class needs it.
Some soft requirements (can be broken, but schedule is still achievable):
- Class time preferred by teachers.
- Teachers’ favorite class.
- Distribution (in time or space) of courses for groups of students or teachers.
The hard and soft requirements will of course depend on the situation.
Algorithm
THE genetic algorithm is relatively simple. For each generation, it performs two basic operations:
- Randomly selects N pairs of parents from the current population and produces N new chromosomes by performing a crossover operation on a pair of parents.
- Randomly selects N chromosomes from the current community and replaces them with new ones. The algorithm does not select chromosomes for replacement if they are among the best chromosomes in the company.
These two operations are repeated until the best chromosome reaches a fitness value equal to 1 (meaning that all classes in the schedule meet the requirement). As mentioned earlier, the genetic algorithm keeps track of the best M chromosomes in the population and ensures that they will not be replaced as long as they are among the best chromosomes.
Main application screen
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