I was doing some research over my christmas break and stumbled upon a concept called Neutral Networks (which sounds rediculously close to Neural Networks, but oh well).
Basically the idea is that during the course of evolution, many point mutations made to an organism's genome are neither positive nor negative with respect to it's reproductive fitness; they are "neutral". A good example of this is the many-to-one mapping for encoding various amino-acids from codons.
Presumably there are many neutral mutations that are often connected by a single point mutation, thus forming "Neutral Networks" embedded in the fitness landscape. The theory is that this is advantageous because it allows the evolving organism to escape local-optima by horizontally sliding (whatever exactly "horizontal" means in N-dimensions) along flat plateaus of homogeneous fitness until an even higher point is stumbled upon. Much research on this is being done at Sussex University.
So, if this is true, then one would assume that evolution could occur even with very low rates of mutation... there would be no need to "jump" off of local optima.
I tested this hypothesis by altering the mutation function in my software. Rather than applying Gaussian or Cauchy distributed mutation (as is standardly done in Evolutionary Programming and Fast Evolutionary Programming, respectively), I chose to use a small, fixed-size constant that would either be added to or subtracted from each linkweight of my Neural Network with equal probability. The results have been nice; this new (and considerably simpler) mutation strategy significantly outperforms the previous method I was using.
However, I did find that it was not feasible to use arbitrarily small constants because: 1) evolution would be too slow, and 2) the changes in fitness need to be larger than the "thermal noise" inherent in the evaluation function (note: I am not working with fixed training sets, therefore the feedback function is a bit noisy).
TWO TYPES OF NEUTRAL NETWORK SYMMETRY
This concept was not mentioned in the literature, but I realized that there are actually two distinct forms of Neutral Networks; one nested inside the other. Basically there are two forms of symmetry that can lead to Neutral Networks.
The first form is symmetry of summed fitness; two different behaviors may be equally fit although for differing reasons. The second form is symmetry of representation; two internally different systems (e.g. Neural Networks) can lead to the same external behavior, which then necessarily implies equal fitness.
I believe that biological systems make use of both forms of Neutral-Network symmetry during the evolutionary process.