Genetic Algorithm Approach to a Concurrent Real-Time Optimization Problem in the Embedded System Design Process

In this paper, we present a genetic algorithm for a concurrent real-time optimization problem occurring in the embedded system design process. The problem consists of two concurrent phases, each impacting the other in real time. In the first phase, parameters are selected for optimization, and in the second, the parameters are optimized and their choice is validated in real time. During the implementation of the embedded system, unexpected situations can arise, each of which can be solved in many ways; each way, in turn, may require the execution of different unexpected tasks. However, identifying the optimal path to follow is significantly challenging. Furthermore, some of the proposed solutions to the problem may not yield appropriate results. The proposed algorithm generates a certain number of individuals and evolves them using genetic operators, performing the proper optimization and comparing the results.