Jumbofive

Hello Dr. Stanley! I have been following your work as well as the work of individuals under Dr. Risto Miikkulainen. I am enthralled by your discovery with PicBreeder and the idea of designing towards novelty. My questions for you are: In what context/medium do you see evolution towards novelty the most intriguing for future use? Second, you mentioned in one of your talks about the idea of novel generation possibly never creating the same solutions again (you related it to the idea of humans not being created in a new generation of earth.) If that is true, would it be possible to to generate populations that are rewarded through novelty, and then use a separately trained decision maker to drive selection of "good" solutions towards traditional objective ideas (Or to use your analogy, have a decision maker only select individuals that are "human")? Thank you.

I really appreciate your insight and for you taking the time to answer my questions! I have one more follow up if you don't mind. In my research throughout computational evolution/neuroevolution I have noticed a pattern of training on only single objectives or a combined weighted objective of some overall goal. The problems that arise from these single objectives is what novelty search is attempting to get away from (Non-robust solutions, human bias, parameter tuning, etc.). My question is though, has there been any research on evolved solutions using many n-objective problems like say n > 10? I guess what I am trying to ask is, do you think that there is a point where enough objectives would result in similar quality solutions to that of novelty search?