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2020 | OriginalPaper | Chapter

Algorithm for Optimization of the Content of the Training Course Practical Part Using the Artificial Immune System

Authors : Irina Astachova, Ekaterina Kiseleva

Published in: Modern Information Technology and IT Education

Publisher: Springer International Publishing

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Abstract

The article presents a model of a training system using the artificial intelligence methods for optimization of certain educational process components. The training system allows the teacher to create and optimize training courses based on the accumulated statistical information. For development of the training system, a hybrid system was chosen combining the advantages of various technologies that allow solving each problem in the optimal way. An algorithm for optimization of the content of the training course practical part using the artificial immune system has been considered. A set of the class’s practical tasks is divided into classes of tasks of similar complexity aimed at achieving similar objectives of the course. The objective function and problem limitations are formulated using H. Markowitz’s model. One of the problem’s objective functions minimizes the correlation between the complexity of tasks of different classes, which allows excluding presence of many single-type tasks in the collection of practical tasks; another objective function maximizes the effectiveness (notion “effectiveness” is introduced in the article) of the collection of tasks. The model’s variables are shares of the total number of tasks selected from each class. For optimization of the given model, a set of Pareto-optimal solutions of a bicriterial problem is found, which allows selecting the optimal relation between the tasks diversity and their effectiveness. The work offers an algorithm for finding the solution of this problem, modified for the artificial immune system. The algorithm suggested in the problem allows obtaining, in a relatively short time, a satisfactory approximation of the Pareto-optimal set for solution of the problem.

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Literature
1.
go back to reference Melnikova, A.A.: Tools for modeling of training multimedia complexes. Ph.D. dissertation (Engineering), Samara (2004). (in Russian) Melnikova, A.A.: Tools for modeling of training multimedia complexes. Ph.D. dissertation (Engineering), Samara (2004). (in Russian)
2.
go back to reference Monakhov, V.M.: Technological Bases of Training Process Design and Construction. Peremena, Volgograd (1995). (in Russian) Monakhov, V.M.: Technological Bases of Training Process Design and Construction. Peremena, Volgograd (1995). (in Russian)
4.
go back to reference Ushakov, S.A.: Development and research of the recognition problem solving algorithms based on the artificial immune systems. Ph.D. dissertation (Engineering), Voronezh (2015). (in Russian) Ushakov, S.A.: Development and research of the recognition problem solving algorithms based on the artificial immune systems. Ph.D. dissertation (Engineering), Voronezh (2015). (in Russian)
5.
go back to reference Vasekin, S.V.: Technological optimization procedures when designing the training process in mathematics: abstract of thesis. Ph.D. dissertation (Pedagogy), Moscow (2000). (in Russian) Vasekin, S.V.: Technological optimization procedures when designing the training process in mathematics: abstract of thesis. Ph.D. dissertation (Pedagogy), Moscow (2000). (in Russian)
7.
go back to reference Stankevich, L.A., Kazanskii A.B.: Immunological security system of a humanoid robot. In: Topical Problems or Protection and Security: Proceedings of the 9th All-Russian Scientific and Practical Conference, Voronezh, no. 5, pp. 145–152 (2006). (in Russian) Stankevich, L.A., Kazanskii A.B.: Immunological security system of a humanoid robot. In: Topical Problems or Protection and Security: Proceedings of the 9th All-Russian Scientific and Practical Conference, Voronezh, no. 5, pp. 145–152 (2006). (in Russian)
11.
go back to reference Kim, J., Bentley, P.J.: Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, South Korea, vol. 2, pp. 1244–1252 (2001). https://doi.org/10.1109/cec.2001.934333 Kim, J., Bentley, P.J.: Towards an artificial immune system for network intrusion detection: an investigation of clonal selection with a negative selection operator. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, South Korea, vol. 2, pp. 1244–1252 (2001). https://​doi.​org/​10.​1109/​cec.​2001.​934333
13.
go back to reference Tarakanov, A.O.: Formal peptide as a basic agent of immune networks: from natural prototype to mathematical theory and applications. In: Proceedings 1st International Workshop of Central and Eastern Europe on Multi-Agent Systems (CEEMAS 1999). St. Petersburg, Russia, pp. 281–292 (1999) Tarakanov, A.O.: Formal peptide as a basic agent of immune networks: from natural prototype to mathematical theory and applications. In: Proceedings 1st International Workshop of Central and Eastern Europe on Multi-Agent Systems (CEEMAS 1999). St. Petersburg, Russia, pp. 281–292 (1999)
15.
go back to reference Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)MATH Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Hoboken (2001)MATH
16.
go back to reference Kashirina, I.L., Ivanova, K.G.: Managing the securities portfolio using a neural network committee. In: System Modeling of Socio-Economic Processes: Proceedings of the 31st International Scientific Workshop School, VSU, Voronezh, Part III, pp. 131–135 (2008). https://elibrary.ru/item.asp?id=28316806. (in Russian) Kashirina, I.L., Ivanova, K.G.: Managing the securities portfolio using a neural network committee. In: System Modeling of Socio-Economic Processes: Proceedings of the 31st International Scientific Workshop School, VSU, Voronezh, Part III, pp. 131–135 (2008). https://​elibrary.​ru/​item.​asp?​id=​28316806. (in Russian)
17.
go back to reference Thompson, P.W.: Mathematical microworld and intelligent computer assisted instruction. In: Kearsley, G.E. (ed.) Artificial Intelligence and Instruction: Applications and Methods, pp. 83–109. Addison-Wesley, New York (1987) Thompson, P.W.: Mathematical microworld and intelligent computer assisted instruction. In: Kearsley, G.E. (ed.) Artificial Intelligence and Instruction: Applications and Methods, pp. 83–109. Addison-Wesley, New York (1987)
Metadata
Title
Algorithm for Optimization of the Content of the Training Course Practical Part Using the Artificial Immune System
Authors
Irina Astachova
Ekaterina Kiseleva
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-46895-8_15

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