Framsticks Artificial Life vs Genetic Algorithms

This app lets you play with simple simulated creatures and their evolution. It features a few genetic representations ("languages"), where each symbol in a genotype defines some characteristic of a creature (a "phenotype"). Each genetic representation has its own methods of mutation (modifying small parts of a genotype) and crossover (exchanging genes of two parents to produce an offspring). The performance of each creature is evaluated in terms of speed on land, speed in water, and height of its center of mass. These criteria can be set as fitness. Since you have mutation and crossover available to change genotypes randomly, you can run an evolutionary process and see how fitness in the population increases. You can also guide evolution according to your preferences by making creatures you like reproduce and generate their random variations. If you understand a genetic language, you can even edit genes manually by deleting and adding genetic symbols, and this way build a creature you want or improve an existing one. The app features some quests to help you familiarize with genetic languages and the properties of evolution such as convergence, diversity, selection pressure, the influence of mutation rate, or the population size. Advanced users can even try their own formulas for fitness, for example to maximize both height and speed simultaneously, or to add even more criteria. Some demonstrations are included as well that show the concepts of directed (external fitness) and undirected (internal fitness) evolution, mutation, swarming, and communication. This app is based on the Framsticks simulator. You can learn more at http://www.framsticks.com/

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.