iTurbo vs Genetic Algorithms

This application performs a Direct Numerical Simulation (DNS) where the Navier-Stokes equations are solved for two dimensional incompressible homogeneous turbulence. These equations are a set of nonlinear partial differential equations. The time stepping is performed with a simple Euler method where the time step is calculated so that the CFL number does not exceeds the given number (should be less than 0.8 otherwise the simulation blows up). The equations are solved in the Fourier space where the calculation of the nonlinear term transforms to a simple multiplication. The main CPU usage is consumed by transforming the vorticity field back and fourth between the Normal space where the time-stepping is performed and the Fourier space where the nonlinear term is calculated.

Genetic algorithms are one of the search and optimisation methods. The aim of optimisation is to increase efficiency in reaching a certain optimal value. Genetic algorithms are based on the mechanisms of natural selection and heredity. The basic genetic algorithm is built from three operations: reproduction, crossing, and mutation. Genetic algorithms operate on populations of coding sequences and use random selection rules to search for the global optimal value. However, these random rules are defined to give the appropriate direction of the search (through various selection mechanisms or scaling of fitness functions). This basic procedure is enhanced by certain genetic manipulations, such as those seen in nature. They include the mechanisms of dominance, diploidy, inversion, and other reconfiguration mechanisms that occur at the chromosome level. Users can design textual or graphical scaling patterns for the fitness function and then use them in simulations. The application also provides a preliminary analysis of statistical data concerning the distribution of the fitness function in the population. The program allows the user to compare non-random procedures (e.g. scaling of fitness functions) by using the same pseudorandom sequence (for procedures requiring randomization, such as selection, etc.) in successive simulations. In order to better visualize the genetic processes taking place during the simulation, some of them are presented to the user using animations. Additionally, some of the data obtained from simulations can be saved and shared.