Genetic Algorithms

Article: Treating Models, Parts, and Features Like Family Parts

Genetic Algorithms are a family of computational models inspired by evolution. These
algorithms encode a potential solution to a specific problem on a simple chromosome like
data structure and apply recombination operators to these structures so as to preserve critical
information. Genetic algorithms are often viewed as function optimizers, although the range
of problems to which genetic algorithms have been applied is quite broad.

So, for building processes I take the best samples and then with some criteria I mix them into one block but this new block is not enough to solve problem, but it is good by the moment. Afterwards with evolution it is found the best model. Once you have you best answer, you have to mutate this into new solutions, new samples........................

In my models


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