Class Scheduler is a list of

time when particular activities or events will happen, or in simple terms, a

schedule. Scheduling is a common way of organizing classes in university or

colleges. It includes the subject of the class and room availability. Class Scheduler is usually, done before the

start 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 high

schools, students have to be occupied and supervised every hour of the school

day, or nearly every hour. Also, high school teachers generally have much

higher teaching loads than the case in universities. As a result, it is

generally considered that university schedules involve more human judgement

whereas high school schedule is a more computationally intensive task. There

are some schools or university assigns the same number of period to all

subjects, but commonly there are variety of length of classes i.e. 9, 8, 7 and

so on, this shows that it is not possible to have a coherent structure to the

time table. Coherent define as the class in each year neatly match up with

classes in other year. However, if the class scheduler is non-coherent it is

more difficult to construct. This complexity gives laborious process in creating

schedules manually.

Evolutionary algorithms

constitute a class of computational paradigms useful for function optimization

inspired from the study of natural processes, which are concurrently subject to

modifications aimed at the determination of the optimal solutions. A

particularly efficient instantiation of evolutionary algorithms is represented

by the genetic algorithm, in which the natural analogy is population genetics.

Genetic algorithms are group of method which solves problem using algorithm

inspired by the processes of neo-Darwinian theory. In a Genetic algorithm, the

performance of a set of candidate solutions to a problem called chromosomes are

evaluated and ordered, then new candidate solutions are produced by selecting

candidates as parents and applying mutation or crossover operators which

combine bits of two parents to produce one or more children. The new set of

candidates is then evaluated, and this cycle continues until an adequate

solution is found.

Schedule problem is a type

of unruly in which events have to be arranged into various number of time

slots, subjects to numerous constraints. The need for the powerful method for

solving 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 possible

candidate in schedules, which vary optimality according to the constraint of

the problem.

Conventional computer-based

program timetabling methods concern themselves in simply finding the shortest

class scheduler that satisfies all constraint, usually done using graph-coloring algorithm and less

optimizing collection of soft constraints, that is to find sets of subjects at

the same time corresponds to finding a coloring such that adjacent nodes have

different colors: each color represents a time slot, and each edge a constraint

that the two vertices which it connects must occupy different slots.

Knowledge-based approaches in solving problems are difficult to develop, are

often slow and can be inflexible because the architecture itself was based on

assumptions regarding the nature of the problem.

Adoption of technological

based approach in creating class scheduler in university will avoid class

schedule conflict and promote the productivity of professor and staff. Applying

the best algorithm that uses the most advance optimization process will be

needed and necessary.

Genetic Algorithm is the

method based on the natural process of biological evolution that can be used to

solve the problems which are difficult to solve with classical methods. Genetic

algorithm is non-deterministic and is used to solve mainly NP-hard problem like

scheduling problem.

This study was created

because Genetic Algorithm can only helped the scheduling problem for only to

generate the schedules if has a conflict. Then NP-hardness

(non-deterministic polynomial-time hard),

in computational complexity theory, is the defining property of a class

of problems that are,

informally, at least as hard as

the hardest problems in NP. Moreover, the class P in which

all problems can be

solved in polynomial time is contained in the NP class.