Proceedings of the 6th international workshop on Hardware/software codesign
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A New Genetic Algorithm for the Optimal Communication Spanning Tree Problem
AE '99 Selected Papers from the 4th European Conference on Artificial Evolution
StreamIt: A Language for Streaming Applications
CC '02 Proceedings of the 11th International Conference on Compiler Construction
A Two-step Genetic Algorithm for Mapping Task Graphs to a Network on Chip Architecture
DSD '03 Proceedings of the Euromicro Symposium on Digital Systems Design
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Packetized On-Chip Interconnect Communication Analysis for MPSoC
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Exploring NoC Mapping Strategies: An Energy and Timing Aware Technique
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Mapping and configuration methods for multi-use-case networks on chips
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Energy-aware mapping for tile-based NoC architectures under performance constraints
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
A methodology for mapping multiple use-cases onto networks on chips
Proceedings of the conference on Design, automation and test in Europe: Proceedings
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Mapping Applications to Tiled Multiprocessor Embedded Systems
ACSD '07 Proceedings of the Seventh International Conference on Application of Concurrency to System Design
MPSoC memory optimization using program transformation
ACM Transactions on Design Automation of Electronic Systems (TODAES)
A multiobjective evolutionary algorithm-based optimisation model for network on chip synthesis
International Journal of Innovative Computing and Applications
MOCell: A cellular genetic algorithm for multiobjective optimization
International Journal of Intelligent Systems - Special Issue on Nature Inspired Cooperative Strategies for Optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Many-core platforms, providing large numbers of parallel execution resources, emerge as a response to the increasing computation needs of embedded applications. A major challenge raised by this trend is the efficient mapping of applications on parallel resources. This is a nontrivial problem because of the number of parameters to be considered for characterizing both the applications and the underlying platform architectures. Recently, several authors have proposed to use multi-objective evolutionary algorithm to solve this problem within the context of mapping applications on network-on-chips. However, these proposals have several limitations: (1) only few metaheuristics are explored (mainly Nondominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2), (2) only few objective functions are provided, and (3) they only deal with a small number of the application and architecture constraints. In this paper, we propose a new framework that avoids all of the problems cited previously. Our framework is implemented on top of the jMetal framework, which offers an extensible environment. Our framework allows designers to (1) explore several new metaheuristics, (2) easily add a new objective function (or to use an existing one), and (3) take into account any number of architecture and application constraints. The paper also presents experiments illustrating how our framework is applied to the problem of mapping streaming applications on an NoC-based many-core platform. Our results show that several new metaheuristics outperform the classical multi-objective metaheuristics such as Nondominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2. Moreover, a parallel multi-objective evolutionary algorithm is implemented in our framework in order to increase the explored space of solutions by simultaneously running several metaheuristics. Copyright © 2011 John Wiley & Sons, Ltd.