Class schedules involve more human judgement whereas high school

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.

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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.