Evolutinary Parameter Optimizer

How to use

Evolutinary Parameter Optimizer is a framework to find optimal parameters for a function. Which means you will need a function and a definition of optimal parameters.

Assume we are trying to find the vector closest to (3,4) when doubled. Our function is also scalar multiplying a vector with 2, and our score for each result is the Euclidian distance to (3,4).

def double_vec(vec):
    # The is the function we are searching the optimal parameters for
    return [i*2 for i in vec]

In order to implement the optimizer, we are going to subclass EvolutinaryParameterOptimizer.

from optimizer import EvolutinaryParameterOptimizer
# Make sure the optimizer module is on PYTHONPATH

class VectorFinder(EvolutinaryParameterOptimizer):

    def __init__(self, vector_to_find, *args, **kwargs):
       	"""
	vector_to_find: An iterable containing numbers.
	"""
	EvolutinaryParameterOptimizer.__init__(self, *args, **kwargs)
	self.vec = vector_to_find

In general, it is not necessary to implement an __init__ function, but it can be helpful, as in this example, to keep the optimizer generic. Otherwise we woul have to hard code the vector in the score function.

The score function of EvolutinaryParamaterOptimizer takes a single argument apart from self, which is the output of the function. It returns a number value, representing how good the solution is. In this case it will be -1 * distance to (3,4). We need the negative because we want to minimize the distance, thus maximize its additional inverse.

def score(self, result):
    return -sum([(i-j)**2 for i,j in zip(result, self.vec)])

Finally, we have to override the mutate function, which tells how to change the parameters at each step. This method gets the exact same parameters as the function being optimized, and calls the submit method of EvolutinaryParameterOptimizer, which also has the exact same signature. The submit method can be called multiple times during mutate, but the preferred way is to call it once, as mutate is called multiple times (EvolutinaryParameterOptimizer.nr_offsprings).

We will mutate the vector by adding random values to each element. Be sure to call from random import random at the beginning of your code.

def mutate(self, vector):
    for i in range(len(vector)):
        vector[i] += (-0.5 + random())*2
    self.submit_params(vector)

Finally, in order to run the optimizer, normally __init__ of EvolutinaryParameterOptimizer takes a single positional argument, the function to optimize, but we have added one more, the vector to find. After init, we need to submit some initial parameters as starting a starting point, and run a number of steps.

if __name__=="__main__":
    # Find the vector (3, 4)
    vector_finder = VectorFinder([3,4], double_vec)
    # Feed some initial parameters
    vector_finder.submit_params([0,0])
    vector_finder.submit_params([-4,-2])
    vector_finder.submit_params([2, 7])

    # Run for 1000 steps
    for i in range(1000):
        vector_finder.run_step()

    args, kwargs = vector_finder.get_best()
    # As we have a single argument, we can just print the 0th entry in args
    print("Found:", args[0])

The whole functioning example can be found under examples/example_0_vector.py

arkocal/evolutinary_optimizer

Evolutinary Parameter Optimizer

How to use