Analysis of Multi-Objective Evolutionary Algorithms to Optimize Dynamic Data Types in Embedded Systems

New multimedia embedded applications are increasingly dy-namic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each tar-get embedded system is a time-consuming process due to the large design space of possible DDTs implementations. Thus, suitable exploration methods for embedded design metrics (memory accesses, memory usage and power consumption) need to be developed. In this work we present a detailed analysis of the characteristics of different types of Multi-Objective Evolutionary Algorithms (MOEAs) to tackle the optimization of DDTs in multimedia applications and com-pare them with other state-of-the-art heuristics. Our results with state-of-the-art MOEAs in two object-oriented multi-media embedded applications show that more sophisticated MOEAs (SPEA2 and NSGA-II) offer better solutions than simple schemes (VEGA). Moreover, the suitable sophisti-cated scheme varies according to the available exploration time, namely, NSGA-II outperforms SPEA2 in the first set of solutions (300-500 generations), while SPEA2 offers bet-ter solutions afterwards