However, as we recorded the history of generated links (PPV, Se and χ2 value after each added link), this allowed us to explore a broader range of model fitness values.
We have verified this proxy using a simple one-locus/two-allele model with fitness values 1 and 1+s (see supplementary figure S1, Additional file 1).
With the obtained prediction model as the fitness value evaluation method, the pump impeller was optimized using the NSGA-II multi-objective genetic algorithm, which finally offered an improved impeller structure with enhanced pressure rise and efficiency.
We used a phylogenetic investigation of pedigreed strains or field samples of demonstrated rope-builders to explore this hypothesis, and provide a fitness value model based on the application of sedimentary physics to this phenomenon.
The prediction curves were analyzed using area under curve (AUC) values which indicate the model fitness for landslide prediction in which value below 0.5 refers low accuracy level whereas value from 0.5 to 1 refers higher accuracy of the models used.
Once this is achieved, and as a means to validate the correlation, one should seek functional models that explain the fitness value of the phenotypic trait in terms of a mechanism exclusively relevant to the shared environments.
The results are summarized in Table 3. Overall, the results are consistent: the smaller the value of λ, the better the model fitness, as indicated by larger likelihood value.
This model thus proposes that the fitness value of oncogenic events is limited and mostly negative in young highly fit tissues optimized by evolution at the germline level, but their fitness effects may increase in aged tissues, facilitating somatic evolution.
In the selection scheme, the model parameters exhibiting the higher fitness value (lower objective function value relative to others) are selected and replicated with the given probability such that the total population size remains constant; then the population members are randomly paired among themselves.
Note that, due to the probabilistic and random nature of SA [ 2], a model with a poor estimated fitness value could be generated and accepted.
