Genetic Algorithm convergence test functions I was working on parallel implementation of Genetic Algorithm with MapReduce. I have found that in many papers authors are referencing OneMAX as problem they used to test scalability and convergence of their algorithm implementations. 

I would like to know more about why this problem is good for testing, and if you could recommend any other exotic and nice fitness functions I could use in tests.

Kind Regards,
ZeKoU
One-max is really only suitable for testing for obvious bugs in your implementation.

There are a few widely used sets of benchmark functions, but they're of rather dubious value in general as well. You don't mention what sort of domain you're planning to work on -- there are binary problems like Knapsack problems, real-valued problems (which may or may not be represented with binary encodings in a given GA), permutation problems like TSP, QAP, and some scheduling problems, etc.

Some of the more popular benchmarks have always been real-valued function optimization problems. Griewangk's function, Rastrigin's function, Ackley's function, Rosenbrock's function, and the De Jong F1-F5 functions have all been used pretty heavily in the literature (as well as others). There are also shifted and rotated versions of several of those problems that attempt to work around some of the more obvious biases and deficiencies such as the optimum of most of them being at the origin.

There have been a few attempts at producing good sets of benchmarks. There was a 2005 CEC workshop that proposed 25 or so real-valued problems. I think there was another workshop in 2010 that might have included a different set of problems, but I'm hazy on the details now.

I haven't been able to provide you with much in the way of concrete information about particular test functions, but hopefully there's enough here to get you started with Google.