Optimizing stressors using genetic algorithm to minimizework-related stress

Abstract

This chapter presents an extension of the existing research on autonomously analysing and grading work-related stress. The use of Genetic Algorithm (GA) are being assessed to minimize the stressors both physical and psychological that gives rise to work-related stress or workplace stress, for their ability to produce new solutions by combining existing solutions based on a fitness value. This effort is aimed at enabling employers to manipulate the stressors and prevent stress within the workplace with respect to changes in workload. Workplace stress can be considered as an event that affects people from all professions in some form or the other. This is of widespread concern and requires intervention as a preventive measure that aims at reducing work stress. Intelligent Multi-Agent Decision Analyser (IMADA) is a multi-agent-based hybridized autonomous model, that can analyse work-related stress data. The model was designed to process data automatically optimizing productivity and predictability. The interactive model provides the user with an automated mechanism that can provide a first step towards the identification and prevention of work stress, andmaking perceptible changes to the working environment.