Class Scheduler is a list oftime when particular activities or events will happen, or in simple terms, aschedule.
Scheduling is a common way of organizing classes in university orcolleges. It includes the subject of the class and room availability. Class Scheduler is usually, done before thestart of the semester; to avoid constraint in both faculty and students. High school schedule are quite different from university schedules. The main difference is the fact that in highschools, students have to be occupied and supervised every hour of the schoolday, or nearly every hour. Also, high school teachers generally have muchhigher teaching loads than the case in universities. As a result, it isgenerally considered that university schedules involve more human judgementwhereas high school schedule is a more computationally intensive task. Thereare some schools or university assigns the same number of period to allsubjects, but commonly there are variety of length of classes i.
e. 9, 8, 7 andso on, this shows that it is not possible to have a coherent structure to thetime table. Coherent define as the class in each year neatly match up withclasses in other year. However, if the class scheduler is non-coherent it ismore difficult to construct. This complexity gives laborious process in creatingschedules manually.Evolutionary algorithmsconstitute a class of computational paradigms useful for function optimizationinspired from the study of natural processes, which are concurrently subject tomodifications aimed at the determination of the optimal solutions. Aparticularly efficient instantiation of evolutionary algorithms is representedby the genetic algorithm, in which the natural analogy is population genetics.Genetic algorithms are group of method which solves problem using algorithminspired by the processes of neo-Darwinian theory.
In a Genetic algorithm, theperformance of a set of candidate solutions to a problem called chromosomes areevaluated and ordered, then new candidate solutions are produced by selectingcandidates as parents and applying mutation or crossover operators whichcombine bits of two parents to produce one or more children. The new set ofcandidates is then evaluated, and this cycle continues until an adequatesolution is found.Schedule problem is a typeof unruly in which events have to be arranged into various number of timeslots, subjects to numerous constraints. The need for the powerful method forsolving a class scheduler problem is plain by considering the fact that with,say, p professor to be fitted to c classroom and s section, there are p:c:s possiblecandidate in schedules, which vary optimality according to the constraint ofthe problem.Conventional computer-basedprogram timetabling methods concern themselves in simply finding the shortestclass scheduler that satisfies all constraint, usually done using graph-coloring algorithm and lessoptimizing collection of soft constraints, that is to find sets of subjects atthe same time corresponds to finding a coloring such that adjacent nodes havedifferent colors: each color represents a time slot, and each edge a constraintthat the two vertices which it connects must occupy different slots.
Knowledge-based approaches in solving problems are difficult to develop, areoften slow and can be inflexible because the architecture itself was based onassumptions regarding the nature of the problem.Adoption of technologicalbased approach in creating class scheduler in university will avoid classschedule conflict and promote the productivity of professor and staff. Applyingthe best algorithm that uses the most advance optimization process will beneeded and necessary.
Genetic Algorithm is themethod based on the natural process of biological evolution that can be used tosolve the problems which are difficult to solve with classical methods. Geneticalgorithm is non-deterministic and is used to solve mainly NP-hard problem likescheduling problem.This study was createdbecause Genetic Algorithm can only helped the scheduling problem for only togenerate the schedules if has a conflict. Then NP-hardness(non-deterministic polynomial-time hard),in computational complexity theory, is the defining property of a classof problems that are,informally, at least as hard asthe hardest problems in NP.
Moreover, the class P in whichall problems can besolved in polynomial time is contained in the NP class.