A simulation optimization approach for design space exploration of soft real-time embedded systems

In this work, the problem of design space exploration of soft real-time embedded systems is formulated as a stochastic simulation optimization problem. A novel multiobjective genetic algorithm is proposed to address this problem. In the proposed algorithm, design metrics such as price and size are optimized while deadline violations are minimized. Experimental results show the advantages of our approach over previous work that consider no deadline violation is tolerable.