Example of a evaluation function for a trivial symbolic regression problems with 12 inputs using DEAP.

deap_reg_12args.py

import operator
import numpy

from math import sqrt

from deap import gp


# Our dataset is 100 random arrays of 0s and 1s
# and the function we are trying to find is simply
# the sum of every arguments ARG0+ARG1+...+ARG11
dataset = numpy.random.random_integers(0, 1, (100, 12))
targets = numpy.sum(dataset, axis=1)

# Initialization of a trivial primitive set
pset = gp.PrimitiveSet("MAIN", 12)
pset.addPrimitive(operator.add, 2)
pset.addTerminal(1.0)

def evaluate(individual):
    func = gp.lambdify(individual, pset)
    sum_ = 0
    # Compute the output for each input then
    # sum to the rest the squared difference
    # between the target and the actual output.
    for data, target in zip(dataset, targets):
        # The star (*) unpack the list of 12 elements
        # to provide the 12 argument to the function.
        output = func(*data)
        sum_ += (target - output)**2
    rmse = sqrt(sum_ / len(outputs))
    
    # DEAP requires that evaluation function to always 
    # return a tuple
    return rmse,