Sciweavers

Share
GECCO
2008
Springer

Analysis of multi-objective evolutionary algorithms to optimize dynamic data types in embedded systems

8 years 11 months ago
Analysis of multi-objective evolutionary algorithms to optimize dynamic data types in embedded systems
New multimedia embedded applications are increasingly dynamic, and rely on Dynamically-allocated Data Types (DDTs) to store their data. The optimization of DDTs for each target 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 MultiObjective Evolutionary Algorithms (MOEAs) to tackle the optimization of DDTs in multimedia applications and compare them with other state-of-the-art heuristics. Our results with state-of-the-art MOEAs in two object-oriented multimedia embedded applications show that more sophisticated MOEAs (SPEA2 and NSGA-II) offer better solutions than simple schemes (VEGA). Moreover, the suitable sophisticated scheme varies according to the available exploration time, namely, NSGA-II outp...
José Ignacio Hidalgo, José L. Risco-
Added 09 Nov 2010
Updated 09 Nov 2010
Type Conference
Year 2008
Where GECCO
Authors José Ignacio Hidalgo, José L. Risco-Martín, David Atienza, Juan Lanchares
Comments (0)
books