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Experimental results on several benchmark problems show that modeling accuracies have been promoted significantly.
Results for the benchmark problems show that both S3 and ADM are efficient for treating high dimensional reliability problems.
Empirical studies on several commonly used regression benchmark problems show that the proposed approach leads to compact networks that generate competitive results compared with the ELM algorithm.
Experimental results on several classification benchmark problems show that the SFC-SVHC achieves good classification results with a small number of rules.
Computational results carried out on a standard set of 135 benchmark problems show that a simple F&F design competes effectively with prior CMST metaheuristics, rivaling the best methods, which are significantly more complex.
The numerical experiments performed on well-known benchmark problems show that the BCOi is competitive with other methods and it can generate high-quality solutions within negligible CPU times.
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A set of benchmark problems shows that our method is stable and convergent.
Results from evaluating the proposed method on a complex real-world scheduling problem and a theoretical benchmark problem show that the method outperforms both a traditional single-objective approach and the prevailing method for multi-objectivization.
The experimental benchmark problem shows that the Pyhop-h outperforms the existed Pyhop in plan length and time.
Computational results on benchmark problems show the efficiency of the proposed algorithm compared to other heuristics.
The simulations of benchmarks and engineering problems show that the parallel computing method proposed in this paper can give full play to the hardware performance of MIC architecture and effectively improve the computation efficiency of an explicit FE solution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com