A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Complexity of Timetable Construction Problems
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Parallel Multilevel Metaheuristic for Graph Partitioning
Journal of Heuristics
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A hybrid method for solving multi-objective global optimization problems
Journal of Global Optimization
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
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The Parallel.FX Task Parallel Library is the latest tool developed for multicore parallelism optimization using the .NET technology. It is a managed concurrency library that provides optimized managed code for multicore processors using a new thread pool that withstands cancellation, waiting and pool isolation, among many other features. The Task Parallel Library also uses dynamic work stealing techniques for superior scalability. This paper analyzes the performance improvement of using the Task Parallel Library of Parallel.FX when applying a Multi-Objective Evolutionary Algorithm to solve a timetabling problem. For comparative purposes, this algorithm has also been parallelized using threads. The results obtained show that both alternatives allow a reduction in the runtime necessary to solve this problem. However, parallelizing the code using the Task Parallel Library of Parallel.FX has the advantage of being easier and the code size is much smaller than directly programming threads.