Genetic Algorithms with R and Shiny

Following on from this post, here's an application that uses a genetic algorithm to find the maximum of a complex function as the inputs to it are varied.

I've implemented this in R and Shiny and the application is hosted on shinyapps.io here. The application lets a user try to beat the computer and find the optimum by brute force alone. Needless to say, there's no chance of a human managing this in any meaningful time.

As with the previous post, the Rastrigin function is being used as the function to which the optimum inputs are to be sought. The R package GA is being used to find the optimum. Gratifyingly, hardly any code is needed; it is on GitHub here.

The progress of the genetic algorithm towards the goal can be plotted. Here's an example.

As with RapidMiner, convergence towards the optimum is quick, and it usually happens in about 30 generations.

In summary, of course R can do this, but for me the cool part is how concise the code is and by using Shiny how easy it is to show to others.