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The knowledge provided by software measurements could lead to a significant increase in efficiency of evolutionary testing.
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Locality has long been seen as a crucial property for the efficiency of Evolutionary Algorithms in general, and Genetic Programming (GP) in particular.
This work is focused on improving the computational efficiency of evolutionary algorithms implemented in large-scale structural optimization problems.
Representation choice and the development of search operators are crucial aspects of the efficiency of Evolutionary Algorithms (EAs) in combinatorial problems.
Despite the efficiency of evolutionary algorithms is prominent for large scale problems, their running times in terms of CPU time are quite large.
Here we explore the ability of cooperative dynamics to further increase the efficiency of evolutionary strategies.
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.
The efficiency of the evolutionary engineering strategy used herein is promising and the fermentation performance of the xylose-evolved recombinant strain was remarkable when compared to previous reports in the literature (Cai et al. 2012; Zhou et al. 2012).
The operators under consideration naturally arise in the study of evolutionary problems in mathematical physics.
Certainly, we already understand many aspects of sacrifice in terms of evolutionary biology.
In order to demonstrate the efficiency of the inverse evolutionary search, specific forging cases are presented, considering the optimisation of the process parameters aiming the reduction of the difference between the realised and the prescribed final forged shape under minimal energy consumption and restricting the maximum temperature.
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