An introduction to genetic algorithms

If you're looking for more doc / research on GAs, be sure to include 'evolutionary computation' in your searches.

The tough parts to using EC/GA on your problem (which this article doesn't discuss at all, unfortunately) are going to be:

a) figuring out a string representation for your candidate solutions (i.e. because strings are easy to 'mate', 'mutate', etc.)

b) writing a fitness function to evaluate one or more candidates

[a] and [b] are intimately related, of course.

One of the nice things about EC/GA is that it's typically very easy to scale up to take advantage of more hardware. If you have an expensive fitness function, e.g. 'fighting' two chess-board evaluators against one another over an extended series of games, you can just farm those simulations out to as many procs/machines as you have available.

For those who like Lisp, here's a similar article with Clojure as the target language:

http://ethanfast.com/2009/09/ga-framework/

I like the simplicity of your solution.

Here's a presentation I gave on GE and tic-tac-toe as part of senior sem in college, might be helpful for someone just getting into GA (as I was at the time). https://docs.google.com/present/view?id=df49rshq_117sjxbr2ks

edit: and here's the tutorial I used at the time, which I think would complement your tutorial http://www.puremango.co.uk/2010/12/genetic-algorithm-for-hel...

This doesn't look like a good framework for GAs. It assumes that the best solution to a given problem is explicitly provided but if it is, then there's no point in running a GA any more. A good framework would only represent the genetic data in the form of a binary string but evaluate the fitness using an external function that actually extracts meaningful information from the string. Shameless plug: here's an example I wrote in Coffee: https://github.com/fawek/distributed-monalisa-evolution/tree.... It also makes it somewhat easy to distribute the evolution to multiple populations which can reside in different processes or machines.

Another problem with GAs that model individuals as arbitrary binary strings is that they unnecessarily create a lot of invalid strings that don't correspond to the solution space of a given problem. Sometimes it's easy to convert the solution space to the entire sequence range of 0 and 1s but sometimes it's not.

For those that like GA's I recommend checking out Cellular evolutionary algorithm ( http://en.wikipedia.org/wiki/Cellular_evolutionary_algorithm ) It is a GA with the addition that you can only mate with those around you. This tweak significantly improves my GA's.

A simple implementation in Scala I just did for fun:

https://github.com/Mononofu/Simple-GeneticAlgorithm

Just like the Java one, but less code. Usually converges in 10 - 20 iterations.

This nature-inspired and free book also contains some chapters on genetic algorithms: http://www.cleveralgorithms.com/

Evolution is not the life's way to solve problem. It is a cold arithmetic where survival is only way.

I really wish people would stop calling Java "JAVA".