Sentence examples for mutation size parameter from inspiring English sources

We find that the number of species is maximized for intermediate values of a mutation size parameter μ; the result is observed for evolving organisms on a randomly changing landscape as well as in a version of the model where negative feedback exists between the local population size and the fitness provided by the landscape.

Estimates of the mutation scaled population size parameter were higher in the Atlantic Ocean (Θ = 6.07) than in the Indian Ocean (Θ = 2.33) and the Pacific Ocean (Θ = 1.67).

Since there is some uncertainty about the details of the microsatellite mutation process, we consider several plausible mutation schemes, and estimate the variance in mutation size simultaneously with the demographic parameters of interest.

The prior distributions were uniform for mutation-scaled population size parameters theta, that are four times the product of the effective population size and the mutation rate, and mutation-scaled migration rates M, that is, migration rate scaled by the mutation rate, over the range of θ = 0 100 and M = 0 100.

The parameter a mutates at a rate of 0.075, and the mutation size is drawn from a Gaussian distribution (μ = 0, σ = 0.15).

Note that the mutation rate and mutation size are relatively large and that using a single parameter value to determine choosiness is rather simplistic.

Mutation size is sampled from a gamma distribution, with distribution parameters chosen to give a mean mutation size of δavg = 0.6 units and a standard deviation of δsd = 0.4 units.

The gene mutation prior and the prior distribution for the effective population size parameter were set at (0.5, 1.5) and 0.05, respectively.

M igrate-N estimates the posterior distribution of mutation-scaled migration and mutation-scaled effective population size parameters, as well as the marginal likelihood of each gene flow network hypothesis (Beerli 2006).

Table 1 The obtained values for MPSO parameters Size parameter 8 × 11 9 × 18 16 × 30 Population 450 1050 4000 Iteration 10 60 50 Table 2 The obtained values for GA parameters Size parameter 8 × 11 9 × 18 16 × 30 Population 450 1100 4000 Iteration 20 60 70 Probability of crossover 0.7 0.7 0.6 Probability of mutation 0.4 0.3 0.1 Number of members competing in the tournament 3 2 3.

Both approaches have a minimal size parameter.