Computational Intelligence to Speed-Up Multi-Objective Design Space Exploration of Embedded Systems

AbstrAct Multi-Objective Evolutionary Algorithms (MOEAs) have received increasing interest in industry, because they have proved to be powerful optimizers. Despite the great success achieved, MOEAs have also encountered many challenges in real-world applications. One of the main difficulties in applying MOEAs is the large number of fitness evaluations (objective calculations) that are often needed before a well acceptable solution can be found. In fact, there are several industrial situations in which both fitness evaluations are computationally expensive and, meanwhile, time available is very low. In this applications efficient strategies to approximate the fitness function have to be adopted, looking for a trade-off between optimization performances and efficiency. This is the case of a complex embedded