Hybrid Intelligent Information System for Predicting the Number of Test Tasks
The algorithm of optimization of the test tasks of the practical part of the training course using an artificial immune system is considered. The set of practical tasks of the class is divided into classes using discrete clustering. The formulation of the objective function and the constraints of the problem is performed using the G. Markowitz model. One of the objective functions of the task minimizes the correlation between the difficulty of tasks of different classes, which allows us to exclude a large number of tasks of the same type from falling into the practical tasks set, another objective function maximizes the effectiveness of the task sets. Model variables are the shares of the total number of tasks selected from each class. To optimize this model, there is a set of Pareto-optimal solutions to the bicriteria problem, which allows you to choose the optimal ratio between the diversity of tasks and their effectiveness. The paper proposes an algorithm for finding a solution to this problem, modified for an artificial immune system. The algorithm proposed in the problem allows us to obtain a satisfactory approximation of the Pareto-optimal set for solving the problem in a relatively short time.