Genetic Algorithms vs Art of Stat

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.

News: The Explore Data app is now part of the all new Art of Stat app, which contains this and seven other modules. Please consider getting the Art of Stat app for the latest updates and features: https://apps.apple.com/us/app/art-of-stat/id6755374228 ------ The Art of Stat: Explore Data app includes statistical methods for exploring categorical and quantitative data. Obtain summary statistics, contingency tables or correlation coefficients and easily generate bar- and pie charts, histograms, boxplots (including side-by-side boxplots), dotplots or interactive scatterplots that let you color dots by a third variable. Several example datasets are preloaded for you to explore (including instructions on the statistical analysis), but you can also enter your own data or import a CSV file from a cloud account. The following methods are implemented: - Analyzing One Categorical Variable - Comparing Groups on a Categorical Variable - Analyzing the Relationship Between Two Categorical Variables - Analyzing One Quantitative Variable - Comparing Groups on a Quantitative Variable - Analyzing the Relationship Between Two Quantitative Variables (Linear Regression) The app provides: - Frequency tables and bar and pie charts for exploring one categorical variable. - Contingency tables, conditional proportions and side-by-side or stacked bar charts for exploring a categorical variable across several groups or the association between two categorical variables. - Mean, standard deviation and the 5-number summary together with histograms, boxplots and dotplots to explore a quantitative variable. - Side-by-side boxplots, stacked histograms or density plots for comparing a quantitative variable across several groups. - Interactive scatterplots with regression lines to analyze the relationship between two quantitative variables. Correlation statistics and linear regression parameters and predictions. Plots of raw and studentized residuals. The app comes with several example datasets preloaded, which you can directly open in the app to explore the app's various features. You can also type in your own data or upload your own CSV file (which any spreadsheet program can create) and select variables from it. Finally the app includes a basic spreadsheet program called Data Editor to create and edit data. You can also copy & paste data from a spreadsheet app such as Google Sheets, Excel or Apple’s Numbers app. Several sample datasets are provided.