Stepwising vs Genetic Algorithms

Stepwising invites you to explore, play with, and learn about gravity. You can check out Kepler's laws of planetary motion, observe and discover solutions to the 3-body problem, or try to drive a frictionless car around a circular track of ice. The program closely approximates the motion of stars, planets, moons, and other celestial objects moving through space under the influence of their gravitational fields. It does this recursively: an object's position determines the amount of force on it, the amount of force on it determines its acceleration, its acceleration determines its average velocity over a short period of time, and its average velocity determines its next position. If the time interval between calculations is small, a very good approximation of the motion is produced.

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.