Abstract
Evolutionary artificial life systems have demonstrated many exciting behaviors. However, there is a general consensus that these systems are missing some element of the consistent evolutionary innovation that we see in nature. Many have sought to create more "open-ended" evolutionary systems in which no stagnation occurs, but have been stymied by the difficulty of quantifying progress towards such a nebulous concept. Here, we propose an alternate framework for thinking about these problems. By measuring obstacles to continued innovation, we can move towards a mechanistic understanding of what drives various evolutionary dynamics. We propose that this framework will allow for more rigorous hypothesis testing and clearer applications of these concepts to evolutionary computation.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have