What limits the current performance of genetic algorithms and neural networks?The principles underlying these techniques, at least the popular science presentation of these principles, suggests that with enough training they could be improvedas much as desired. It seems that this is not the case though.What are the causes for this? Why can't better results be achieved simply with more computing power? Are there fundamental theoretical reasons for this?I realize this is a very broad question, but I'm sure some of the people here push against these bounds in their work, and I'd like to hear what they have to say.
What limits the performance of evolutionary computing techniques?
ne.neural evol;genetic algorithms;genetic programming