Finally, two methods are presented to cope with the new model, an exact interval branch-and-bound method and an evolutionary algorithm called UEGO (Universal Evolutionary Global Optimizer).
This new method served an evolutionary purpose.
The stochastic mapping method assumes an evolutionary model.
Our method uses an evolutionary model to infer all necessary parameters from the data set itself.
Further, a gene regulatory network (GRN) inference method employing an evolutionary search that makes use of the framework and the scoring metric is also presented.
An initial ensemble of fluid flow simulation models is created where the full range of uncertain parameters are acknowledged using experimental design methods, and an evolutionary algorithm is used for optimization in the history matching process.
Phylogenetic reconstructions based on Bayesian Markov chain methods suggest an evolutionary scenario in which some large brained tropical passerines that invaded more seasonal regions evolved migratory behavior and migration itself selected for smaller brain size.
For example, gene content methods define an evolutionary distance between two genomes based on the percentage of shared homologous genes.
As a test of the utility of the methods in an evolutionary context, simulations were performed under negative selection for protein folding and binding.
These methods combine an evolutionary model with observed DNA sequence data in order to estimate which DME explains the observed data best.
We complement this method through an evolutionary-type element removal procedure that is based on the topological sensitivity such that an advancing front algorithm is gradually removing elements from the design boundary of the domain.
