GENETIC ALGORITHM WITH CENTER BASED CHROMOSOMAL REPRESENTATION TO SOLVE NEW STUDENT ALLOCATION PROBLEM
ABSTRACT: Genetic Algorithm
(GA) is one of the most effective
approaches for solving optimization problem. We have a problem difficulty for
GA in clustering problem. It can be viewed as optimization problem, that is
maximization of object similarity in each cluster. The objects must be
clustered in this paper are new students. They must be allocated into a few of
classes, so that each class contains students with low gap of intelligence and
they must not exceed the class capacity. The intelligence gap of each class
should be low, because it is very difficult to give good education service for the students in the class whose
high diversity of achievements or high variation of skills. We call this
problem as New Student Allocation Problem (NSAP). Initially, we apply GA with
Partition Based Chromosomal Representation (PBCR). But experiments only provide
a small scale case (200 students and 5 classes with same capacities). Then we
try to apply GA with Center Based Chromosomal Representation (CBCR) and we
evaluate it with the same data. We have
successfully improved the performance with this approach. This result indicates
that chromosomal representation design is the important step in GA implementation. CBCR is better than PBCR
in all aspects. All classes generated by CBCR approach have largest gap of intelligence
in each class less than generated by PBCR. CBCR approach can reduce these values
almost a half of the values with PBCR approach.
Author: Zainudin Zukhri,
Khairuddin Omar
Journal Code: jptinformatikagg070001
