In a nutshell, you use mingal by sublcassing mingal.Candidate and passing
your sublclass to mingal.sim. You'll almost certainly want to override at
least the compute_fitness method. Each method's docstring should be
sufficiently explanatory for you to override it when applicable.
In many cases, it will be easiest for you to maintain the bit string gene
representation and override only the compute_fitness method; this lets you
use the mutate and mate methods provided. Of course, if encoding and
decoding such a representation is sufficiently difficult and/or you want more
structured mutation and mating (which might be the case if the distribution of
feasible candidates in your candidate space is sparse) then you can override
mate and mutate as well.
By default, Candidate maximizes the number of '1's in a bit string.
Running python mingal.py will run a sample simulation. See the bottom of
mingal.py for the code which runs this sample.
mingal is released under the MIT license, the text of which follows. That basically means you can do whatever you want with it. However, if you use or modify mingal, I'd love the opportunity to see what you did. In order to decrease the friction of showing me, here's a link to send me an email about it.
Copyright (C) 2011 by Richard Frankel ([email protected])
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