Another project at school this quarter required me to implement the genetic algorithm (GA). It was a bit of a fun project, mostly because the GA is a metaheuristic, so its ripe for reuse. In fact, I used the same GA library on three separate problems. The satisfaction of reuse really motivates me to provide improvements to the library.
The Code
The code was written in Ruby. There are a few examples included that you can run right away. The README should help get started.
Details
Some details might be worth mentioning.
Only a simple GA is implemented. There’s not facilities for multiple populations (islands), nor local searching (like memetic, annealing, etc.), nor co-evolution.
On the other hand, it’s quite easy to use any one of the built-in methods for selection, mutation, and crossover. Methods for mutation and crossover are avaible by just including one of the built-in modules, like UniformMutation or OnepointCrossover. See the examples to get an idea how this is done. The TournamentSelector class will give you an idea of how to implement a selection method.
Finally
In the end, it’s not terribly full-featured, but it’s easy to use and it may prove useful for quick prototyping of an optimization problem.