Machine Learning

Evolutionary Machine Learning (II)

The genome is an exciting subject and the reason behind is the universe of things happening when DNA is converted into proteins. This contains the core concept of the Central Dogma of Molecular Biology, where proteins are obtained via the mechanisms of gene expression ... the subject of this “nablog“.

Recalling the last post, we use 3 matrices in order to model the behaviour of an organism as it transitions through a state space. Today we take on D, the Darwing expression operator or expressor. The elements of this matrix are real values in the interval (0,1]. The effect of multipliying D by M is the strengthening or weakening of the elements of M. Observe that D and M have the same dimension, so the product brings about a matrix of the same size. The biological process of expressing genes is quite complex, but here, in my world, things are kind of cartoonish ... after all, this is just a model.

See how D is generated. Take a look at the file darwin.js (darwin.py) at openlambda under ml, and play around with the way the elements are calculated.

Next time we continue with L, the awesome Lamarck operator ... this one will not be the end of the ride, more is coming ... so lean back and relax ...