An Employment of the Evolutionary Algorithms in Training of Neuro Fuzzy Inference System for Forecasting Personnel Radiation Exposure
Evolutionary algorithms have gained widespread recognition as a viable approach to numerous optimization problems that are characterized by infeasible optimal solutions, owing to the presence of large search spaces and computational limitations. Forecasting personnel radiation exposure can be one of these problems. Radiation exposure poses risks to various practitioners as well as patients in the healthcare facilities. In this study, we model the problem as a specific time series instance. Moreover, we investigate the impact of the training an adaptive neuro fuzzy system using evolutionary algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on the overall performance of forecasting personnel radiation exposure. The results show that GA and PSO could provide effective solution. On the other hand, they might be highly affected by the initial state of the fuzzy inference system leading to unstable performances. We recommend further experimentation with a combination of other advanced optimization and machine learning methods to assure the most effective results.