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The IMO, most interesting work in EA is in 'search space illumination', MAPElites, and it's variations are a great example.
by zardo 9y ago
The IMO, most interesting work in EA is in 'search space illumination', MAPElites, and it's variations are a great example.
- sevensor 9y agoThanks for pointing this out. I had a look at http://www.evolvingai.org/files/1504.04909v1.pdf http://www.evolvingai.org/files/1504.04909v1.pdf. It's an interesting read, although I'm not sold on the value of feature space exploration. I think it muddies the waters around the problem of balancing optimization with diversity, by lumping decisions and constraints together. What's to stop it from over-sampling regions of the decision space that have a steep gradient in the other features?
- zardo 9y agoWhat's to stop the discovered solutions from being similar? Nothing but the dimensions of interest. If the best solutions with respect to features a, b, and (a&b) are all the same, then it does not seek different solutions. This may indicate you chose reduntant features though. Here's an interesting application that provides some motivation. https://arxiv.org/abs/1407.3501 https://arxiv.org/abs/1407.3501
- sevensor 9y agoRegarding the similarity of solutions: If, in a particular region, your model outputs are particularly sensitive to the inputs, that region can be overrepresented in a feature map. What I mean by this is that we cannot effectively choose between the solutions in this region -- the region is small compared to our ability to control inputs. Overrepresenting such a region in a population-based search starves the rest of the problem space of the algorithm's attention. Thanks for the link, by the way -- this is a particularly interesting application.