Computational difficulties of bilevel linear programming
Operations Research
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Sun Grid Engine: Towards Creating a Compute Power Grid
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Professional VB.NET 2003, 3rd Edition
Professional VB.NET 2003, 3rd Edition
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
High-Performance Computing: Clusters, Constellations, MPPs, and Future Directions
Computing in Science and Engineering
Distributed computing in practice: the Condor experience: Research Articles
Concurrency and Computation: Practice & Experience - Grid Performance
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Exploring the performance limits of simultaneous multithreading for memory intensive applications
The Journal of Supercomputing
Recent trends in the marketplace of high performance computing
Parallel Computing
Optimization of Transit Priority in the Transportation Network Using a Genetic Algorithm
IEEE Transactions on Intelligent Transportation Systems
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The optimization of Road Space Allocation (RSA) from a network perspective is computationally challenging. An analogue to the Network Design Problem (NDP), RSA can be classified NP-hard. In large-scale networks when the number of alternatives increases exponentially, there is a need for an efficient method to reduce the number of alternatives while keeping computer execution time of the analysis at practical levels. A heuristic based on genetic algorithms (GAs) is proposed to efficiently select Transit Priority Alternatives (TPAs). The proposed framework allows for a TPA to be analysed by a commercial package that is a significant provision for large-scale networks in practice. We explore alterative parallel processing techniques to reduce execution time: multithreading and High-Throughput Computing (HTC). Speedup and efficiency are compared with that of traditional sequential GA, and we discuss both advantages and limitations. We find that multithreading is better when using the same number of processors, but HTC provides expandability.