algorithmic modeling for Rhino
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I'm not a specialist either in neuroscience or genetic algorithms, but in my experience problems that are easy for the human mind are ill suited for genetic algorithms. It seems to be pretty directly inverse relationship. This is my amateurish theory: Our minds are incredible at signal processing and pattern recognition. We are also not too bad at modeling reality and making predictive outcomes about systems (at least in the short term). Genetic Algorithms have no knowledge of the problem they've been given. They are more or less blindly stumbling around a fitness landscape trying to find the lowest or highest point. This is good for optimizing well defined problems with complex, but smooth fitness landscapes.
When there are (infinitely) many potential outcomes that meet only a single binary criteria (e.g. circles intersecting or not) its not going to do so well. On the other hand, we look at the problem and not only are we able to solve it nearly instantly, but we'll also be considering the very human and subjective element of aesthetic.
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