Bayesian evolutionary analysis provide a statistically sound and flexible framework for estimation of evolutionary parameters. In this method, posterior estimates of evolutionary rate (μ) are derived by combining evolutionary information in the data with researcher’s prior knowledge about the true value of μ. Nucleotide sequence samples of fast evolving pathogens that are taken at d...

Microarray technology has produced massive expression data that are invaluable for investigating the genome-wide evolutionary pattern of gene expression. To this end, phylogenetic expression analysis is highly desirable. On the basis of the Brownian process, we developed a statistical framework (called the E(0) model), assuming the independent expression of evolution between lineages. Several e...

Analysis of the degree of intraspecific morphological polymorphism during the evolutionary history of trilobites using an informatic approach(1) provides striking evidence of a long-suspected but previously unsubstantiated pattern: degrees of polymorphism are markedly higher in phylogenetically basal, stratigraphically early species. This unequivocal pattern prompts further exploration of the r...

In this paper, stable modifiable walking pattern algorithm is proposed using evolutionary optimized central pattern generator (CPG). Sensory feedback pathways in CPG are proposed, which use force sensing resistor (FSR) signals. For the optimization of CPG parameters, two-phase evolutionary programming (TPEP) is employed. Modifiable walking pattern generator (MWPG) generates position trajectory ...

We present and analyze a class of evolutionary algorithms for unconstrained and bound constrained optimization on R(n): evolutionary pattern search algorithms (EPSAs). EPSAs adaptively modify the step size of the mutation operator in response to the success of previous optimization steps. The design of EPSAs is inspired by recent analyses of pattern search methods. We show that EPSAs can be cas...

An evolutionary model of classifier synthesis is presented. The CellNet system for generating binary pattern classifiers is used as a base for experimentation [5]. CellNet is extended to include a competitive co-evolutionary mechanism, where patterns (prey) evolve as well as pattern classifiers (hunters). This is facilitated by the addition of a set of topologically-invariant camouflage functio...