Automated Leadership Selection Educational Projects Based on Genetic Algorithms

Purpose or research. The purpose of the study is to conduct a research of the possibility to use automated methods of finding a solution for the task of forming a selection of candidates in solving a wide range of business problems with the possibility of the influence of such factors as: requirements for the quality of business processes, restrictions on the competence of candidates, the employment of candidates in other processes of the company, planned urgency of fulfilling tasks, the amount of tasks in the pool and expected tasks, tasks characteristics, private policy of the company when selecting candidates, company's policy on risks.

Methods. A typology of innovative business tasks is given by the subject area, the range of target results, the duration of execution, etc. External and internal factors are considered to ensure the effective operation of the project team and the success of the project. There are offered sets of attributive characteristics for assessment of projects quality and potential projects. The structure of the algorithm for solving the problem of creating a selection of candidates for solving a set of problems is considered. Prerequisites for using principles of genetic programming in solving the problem under consideration are given. Search algorithm implementation parameters, criteria and constraints are defined. Algorithm was implemented as well as modeling in the Jupyter Lab v2 environment. The results are described in the text. A relative analysis of practical effectiveness of the algorithm was carried out depending on the modeling parameters to justify the selection of their values.

Results. The task of creating a selection of candidates for the implementation of projects pool considering a number of factors was described during the study. An approach to solving the problem based on a genetic heuristic search algorithm has been developed. A numerical simulation was performed in Jupyter Lab v2. Simulation results were analyzed, and algorithm parameters were selected.

Conclusion. The proposed approach allows not only automatize the selection of managers based on the accumulated data history, but also to adjust the established process to change the vector of organization development. The interaction of education and informatics (information technology) can enrich and expand the field of both sciences in the field of recruitment of innovative project teams. Their object analysis, supplemented by genetic programming capabilities, together allows you to achieve specified qualities of the head of innovative project teams that help maximize business benefits while minimizing material costs. As a result of computational experiments using mathematical apparatus and genetic algorithm technologies, it is necessary to emphasize the possibility of extrapolating such approaches to any level of implementation of innovative projects.

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