Detailed data analysis of the designed CISK-PX protein demonstrates positive impacts of knowledge-based secondary structure and solvation predictions and structure-based sequence profiles on the efficiency of the evolutionary-based protein design method.
Results from several experiments indicate (a) significant impact of IP on the optimality of cluster formation and evolution decisions, and (b) efficiency of the developed evolutionary algorithms in handling the problem's complexity, and the emergent behavior of matching.
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.
Accurate results are obtained proving the efficiency of the neuro-evolutionary methodology.
Altogether, these results emphasize the efficiency of the in vivo evolutionary engineering strategy for improving ethanol tolerance, and the link between ethanol tolerance and diploidization.
The effectiveness and efficiency of the Bi-directional Evolutionary Structural Optimization (BESO) method has been demonstrated on the minimization compliance problem with fixed external loads.
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.
Moreover, the efficiency of evolutionary algorithms to predict the concentrations of the anti-inflammatory drugs in ultrapure and river water samples, artificially polluted in the laboratory and their robustness against large errors during the analysis of the composite samples, are evaluated.
Early studies, albeit based on an unrealistic genotype-phenotype map, suggested that plasticity could improve the efficiency of an evolutionary search by smoothing fitness landscapes [ 75].
Representation choice and the development of search operators are crucial aspects of the efficiency of Evolutionary Algorithms (EAs) in combinatorial problems.
