APPLICATION OF GENETIC ALGORITHMS FOR SOLVING MULTI-CRITERION CHOICE PROBLEMS IN FORMING THE COMPOSITION OF EXPERT GROUPS
DOI:
https://doi.org/10.32782/2786-9024/v4i6(38).359118Keywords:
genetic algorithms, expertise, expert group, combined method, expert assessment, optimal solution, multi-criteria optimization, group composition, mathematical methods, statistical methods, chromosomes, gene, crossover, mutation, fitness function.Abstract
The article is devoted to the solution of an urgent scientific and applied task – the development and substantiation of a combined method for forming expert groups, the operating algorithm of which integrates statistical approaches, mathematical modeling, expert assessment methods, and the apparatus of genetic algorithms. The paper substantiates the necessity of optimizing the expert selection process to minimize errors during the examination procedure. A comprehensive scheme for forming the optimal composition of an expert group is proposed, based on a systematic combination of quantitative and qualitative indicators. Within the framework of the study, a complex optimization problem of multi-criteria selection of specialists for technical, social, and economic examinations is formalized. A scheme for combining statistical methods and a genetic algorithm in the formation of expert groups is presented. The mathematical model of the problem is based on maximizing a fitness function that considers a number of critical parameters: an individual expert competence index, a professional experience coefficient, and the degree of consistency of the candidate’s previous assessments. Particular attention is paid to the application of genetic algorithms for searching for optimal solutions in a large space of alternatives. The use of evolutionary mechanisms of selection, mutation, and crossover allows for the effective resolution of the multi-criteria selection problem, ensuring high precision in group formation. The results confirm that the synthesis of genetic algorithms with expert and mathematical methods significantly increases the reliability of forecasts and optimizes decision-making processes.
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