ConnectXchange vs Genetic Algorithms

This app is designed as an immediate reference tool for students of art and its histories and those with an interest and enthusiasm, who need a quick reference and aid memoir for educational, academic or clarification purposes. The Connect app is made of three complimentary sections which will provide you with a quick reference guide. - Movements and Styles - a reference overview of the key movements and styles throughout the history of art. Each entry provides a brief description, identifies key artists, key works and reliable links for further study collections and information - Timeline – offers a chronological survey, letting you to scroll quickly through the centuries of history for context and reference of key moments in global history and in art, with direct links back to the Movements and Styles - Glossary – an essentials glossary of terms and colours.

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.