Curriculum plan optimization with rule based genetic algorithms

In corporations, accurate planning should be applied to manage the in service training task within an optimum time period and without hindering the working tempo of the employees. For this reason, it is better to consider the curriculum planning task as a timetabling problem. However, when the timetables are prepared manually, it may turn out to be a complicated and time consuming problem. In this study, an effective solution to the curriculum planning problem by using a rule ? based genetic algorithm is put forward. The data, which is used by the fitness function of the GA to obtain the results, is the prerequisite rule set of the modules of the training program. The contribution to the literature is handling the structure of its data set successfully, despite tightly related rules among the modules. The modules of a training material were ranked effectively and while performing the ranking process, parameter tuning for GA was done to determine the best parameter combination of GA. The tests were done for two different amounts of modules. The results were then compared with the suggestion of an expert trainer by using Spearman rank correlation test, which is nonparametric, and the best parameter combination of the GA giving the most similar result to that of the expert?s was determined. According to the tests, the results gathered were considered to be 98.53 percent reliable for the smaller size of module ranges (chromosomes) and 97.06 percent reliable for the larger size of module ranges when compared with the corresponding suggested module range. Same tests were repeated with a control data set, having the same characteristics with the first one and two different sizes, and the results verified that same parameter combinations give the same successful module ranges in the same reliability percentages. Keywords: Genetic algorithm, rule base, curriculum plan optimization, Spearman rank correlation.