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)
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Storage Management Programmable Process
Storage Management Programmable Process
Theory of Modeling and Simulation
Theory of Modeling and Simulation
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
PSFGA: parallel processing and evolutionary computation for multiobjective optimisation
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
SCOPES '07 Proceedingsof the 10th international workshop on Software & compilers for embedded systems
DEVS-based simulation web services for net-centric T&E
Proceedings of the 2007 Summer Computer Simulation Conference
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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
Considerations in engineering parallel multiobjective evolutionary algorithms
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
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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 searching space of possible DDTs implementations. This results in the minimization of embedded design variables (memory accesses, power consumption and memory usage). Till date some effective heuristic algorithms have been developed in order to solve this problem, however unreported, as the problem is NP-complete and cannot be fully explored. In these cases the use of parallel processing can be very useful because it allows not only to explore more solutions spending the same time but also to implement new algorithms. This research work provides a methodology to use Discrete Event Systems Specification (DEVS) to implement a parallel evolutionary algorithm within a Service Oriented Architecture (SOA), where parallelism improves the solutions found by the corresponding sequential algorithm. This algorithm provides better results when compared with other previously proposed procedures. In order to implement the parallelism the DEVS/SOA framework in utilized. Experimental results show how a novel parallel multi-objective genetic algorithm, which combines NSGA-II and SPEA2, allows designers to reach a larger number of solutions than previous approximations. This research also establishes DEVS/SOA as a platform for conducting complex distributed simulation experiments.