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Experimental evidence supports that evolutionary MCMC (or eMCMC) exploits the efficiency of simple evolutionary algorithms while maintaining the robustness of MCMC methods and outperforms either approach used alone.

Recursive forecast combining generally yields a better balance of type I and type II errors than any of the individual forecasting methods, and outperforms the naïve predictions.

The experimental results obtained indicate that our approach achieves comparable performance against two cost-sensitive methods and outperforms the non-cost-sensitive method AdaBoost.

The predicted codon anticodon mappings often agree with experimental results, in particular for the HMM method, which is better than the other two methods and outperforms the method of wobble parsimony.

In this paper we propose a nonrigid image registration algorithm for coronary angiography images that is significantly faster than multiresolution and iterative blocking methods and outperforms competing algorithms evaluated on the same data sets.

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The simulation results prove that the proposed AHAIS is an efficient method and outperforms DE, PSO, opt-aiNet and IA-AIS in convergence speed and solution accuracy.

The simulation results indicate that the proposed AINet-SL is an efficient method and outperforms DE, opt-aiNet, IA-AIS and AAIS-2S in convergence speed and solution accuracy.

On the MHCPEP-UPDS version of the HLA-DR4 dataset, the 5-spectrum kernel outperforms the other two prediction methods and CTD outperforms the LA kernel.

Experiments on the BAliBASE dataset [ 12] show that our approach is competitive with the best progressive methods and significantly outperforms most non-progressive algorithms.

This performance is comparable with those of other well-known methods and ICN outperforms other methods when a candidate disease gene is not directly linked to known disease genes in a network.

As we can see, the proposed algorithm yields comparable performance to the method YSH and outperforms LSQ when BR2 is higher than BR1.

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