My 8th grade science fair project studied a very simple version of this problem! I used genetic algorithms to have a 6-legged robot relearn to walk after 1 leg was injured. It won 1st grand prize in Texas in 2004 :)
How is that a very simple version of the problem :)
The robot hardware was the very simple part. Each leg only had 1 degree of freedom and was binary, just contracted or extended with no in-between. It was the Stiquito robot http://en.wikipedia.org/wiki/Stiquito , which is dirt cheap and uses muscle wire (nitinol) to move the legs.
Its more of a 'brute force' method ... genetic algorithms take one or more variables and randomly change them. Then keep iterating on a solution that works - you don't need to know why something works only that it works better than the previous solution.
While genetic algorithms are highly iterative and often computationally expensive approaches to randomized search, they are vastly more efficient actual brute force methods. It would be better to say "it is more of a 'randomized search' method", especially with the connotations associated to actual brute force techniques.