Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
System-level power optimization: techniques and tools
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Data and memory optimization techniques for embedded systems
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Practical Data Structures Using C/C++ with 3.5 Disk
Practical Data Structures Using C/C++ with 3.5 Disk
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
SCOPES '07 Proceedingsof the 10th international workshop on Software & compilers for embedded systems
Memory-access-aware data structure transformations for embedded software with dynamic data accesses
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special section on the 2002 international symposium on low-power electronics and design (ISLPED)
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Transforming set data types to power optimal data structures
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Optimization of dynamic data types in embedded systems using DEVS/SOA-based modeling and simulation
Proceedings of the 3rd international conference on Scalable information systems
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Optimization of dynamic memory managers for embedded systems using grammatical evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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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 Multi-Objective 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 outperforms SPEA2 in the first set of solutions (300-500 generations), while SPEA2 offers better solutions afterwards.