FRACTIONAL DYNAMICS IN GENETIC ALGORITHMS

Dynamics of Genetic Algorithms in Optimization Problems

In a recent paper [1], some of us have studied the dynamics of the Monte Carlo simulation algorithm in several very different optimization problems. The optimization problems that were investigated in [1] include the travelling salesman problem (TSP) [2], the lattice version of the protein folding problem (PFP) [3], x-ray data analysis (XDA) and a multi-variable function. In the above studied c...

Determining evolutionary dynamics in complex networks using genetic algorithms

Gene regulatory networks are one of the most important goals in the novel discipline of system biology. These regulatory networks, through the interaction of multiple genes, control and guide the proteic interactions and, in fact, the cellular behaviour. Understanding this regulation is, therefore, essential in the investigation in organogenesis, during the embryonic stages of the organisms, an...

Application of genetic algorithms to problems in computational fluid dynamics

P 1 . 7 Genetic Algorithms in Geophysical Fluid Dynamics

The genetic algorithm (GA) is an optimization tool that has shown great success at solving problems not amenable to easy solution via more traditional means (such as the traveling salesman problem, solved by Koza 1992). Since most CFD problems have not been traditionally posed in terms of optimization, GAs have not yet been widely used in this field. The goal of this work is to demonstrate the ...

Fractional Brownian dynamics in proteins.

Correlation functions describing relaxation processes in proteins and other complex molecular systems are known to exhibit a nonexponential decay. The simulation study presented here shows that fractional Brownian dynamics is a good model for the internal dynamics of a lysozyme molecule in solution. We show that both the dynamic structure factor and the associated memory function fit well the c...