GA Python

Here is a simple example of using a GA in Python using PyOptSparse and my wrapper available here.

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def rosen(x):

f = (1 - x[0])**2 + 100*(x[1] - x[0]**2)**2
c = []

return f, c

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Here is where we define the problem and choose an optimizer. Various options exist, I've set a few, see documentation for details.

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from pyoptsparse import NSGA2

# choose optimizer and define options
optimizer = NSGA2()
optimizer.setOption('maxGen', 200)
optimizer.setOption('PopSize', 40)
optimizer.setOption('pMut_real', 0.01)
optimizer.setOption('pCross_real', 1.0)

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Now we can run the optimizer and parse the results.

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from pyoptwrapper import optimize

x0 = [4.0, 4.0]
lb = [-5.0, -5.0]
ub = [5.0, 5.0]

xopt, fopt, info = optimize(rosen, x0, lb, ub, optimizer)
print 'results:', xopt, fopt, info

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results: [ 0.80973481  0.65449819] 0.0363382656261 {'code': {}, 'fcalls': 3396, 'time': 1.1790099143981934}

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NSGA, like many genetic algorithms, doesn't have any speicific convergence criteria other than the maximum number of generations. I set it at 200 in this case. Notice that the answer is ok, but not super great.

Let's also try with SNOPT and start fairly far away (and I won't supply gradients):

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from pyoptsparse import SNOPT

optimizer = SNOPT()

xopt, fopt, info = optimize(rosen, x0, lb, ub, optimizer)
print 'results:', xopt, fopt, info

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results: [ 0.99969953  0.99939864] 9.03093498134e-08 {'code': {'text': 'optimality conditions satisfied', 'value': array([1], dtype=int32)}, 'fcalls': 122, 'time': 0.04616093635559082}

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We have the answer to high precision, and its fast and repeatable. For something that is differentiable a gradient-based method is preferable, but if the function space is fundamentally noisy, discrete, or highly multi-modal then a GA or other gradient-free method can be effective.

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