A Hybrid Genetic - Differential Evolution Algorithm (HybGADE) for a Constrained Sequencing Problem
MetadataShow full item record
— For researchers, evolutionary algorithms are mostly preferable because of their effectiveness in finding the optimum solutions to many problems. Among these problems, sequencing is one of the most popular. In daily life, it is a must to find the best solution to a sequencing problem in order to save time, money and labour. Education is also one of the application areas of optimization where sequencing matters. In this paper, a hybrid genetic - differential algorithm is introduced, which finds better solutions to sequencing problem in education. The correct order of educational data is crucial because it directly affects the students' performance. In this study, educational material of Database course in Ege University Tire Kutsan Vocational School Computer Programming Department is used as the data set with two different evolutionary algorithms (EA). In these data sets, there are some constraints which should be considered while sequencing. We called them “prerequisites” that tells us the rules about the order of the modules of a course. That is why, the study can be considered as an application of Precedence-Constrained Sequencing. The sequencing performances of pure genetic algorithm (GA) and hybridized differential evolution (DE) with genetic algorithms (HybGADE) are compared with a computer program implemented. It is observed that, HybGADE can be used with 99.54% of reliability where pure GA has an effectiveness of 98.53%. The percentage of the students passing the class has been observed for four years. The ratio of students passing the class has increased from 39% to 65%, which can be considered as a remarkable increase.