Neuro-Genetic Hybrid System for Management of Organizational Development Measures

Abstract

Current practical experience in measuring the effectiveness of organizational development activities is largely based on the evaluation of surveys. In this paper we present an approach based on an artificial neural network with elements of a fuzzy approach and a genetic algorithm to control organizational development. Based on genetic algorithms, the organizational development measures are initiated, selected, combined or mutated with the goal of finding the best possible solution for each concrete case. Since many variables have the uncertain set of their values, the use of a hybrid neuro-fuzzy mechanism makes it possible to analyze the behavioral components up to the combinations of needs and thereby select the appropriate organizational development measures. The system is designed to ensure the long-term effectiveness of organizational development measures. We supplement the previously known measures of organizational development with technology-based in order to increase the degree of automation in practice. This article is intended as an orientation for other scientists who are researching the same topic and are interested in the current state of the art, as well as for companies who want to ensure compliance with internal company rules using digital tools.