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I played a bit with the "more faithful" genetic algorithms, got some promising results (getting out of the local minima better). The key is to use hierarchical
by sklogic 11y ago
I played a bit with the "more faithful" genetic algorithms, got some promising results (getting out of the local minima better). The key is to use hierarchical encoding in your chromosome: instead of directly encoding traits/functions/properties/whatever, encode a program (maybe even in a Turing-complete representation) which in turn generates those traits/functions/properties/whatever, or even generates a next level of code. This is closer to how the biological systems work, and allows for much higher sensitivity to small changes.
- ionforce 11y agoDo you have more information about this approach? I've never heard about it until now.
- sklogic 11y agoI tried to dig out the references from my mess of bibliography (I worked on this topic long before Mendeley and alike appeared), unsuccessfully. I remember that I ran into this technique independently first (in a context of encoding expression trees) and then found a number of other examples in the literature (e.g., encoding the neural network topology, etc.).