Source code for surrogate.crossover.cxUniformPartialMatch
# Copyright 2016 Quan Pan
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# http://www.apache.org/licenses/LICENSE-2.0
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# Author: Quan Pan <quanpan302@hotmail.com>
# License: Apache License, Version 2.0
# Create: 2016-12-02
import random
[docs]def cxUniformPartialMatch(var1, var2, prob=0.5):
"""Executes a uniform partially matched crossover (UPMX) on the input
individuals. The two individuals are modified in place. This crossover
expects :term:`sequence` individuals of indices, the result for any other
type of individuals is unpredictable.
:param var1: The first variable participating in the crossover.
:param var2: The second variable participating in the crossover.
:param prob: Independent probabily for each attribute to be exchanged.
:returns: A tuple of two variables.
Moreover, this crossover generates two children by matching
pairs of values chosen at random with a probability of *indpb* in the two
parents and swapping the values of those indexes. For more details see
[Cicirello2000]_.
This function uses the :func:`~random.random` and :func:`~random.randint`
functions from the python base :mod:`random` module.
.. [Cicirello2000] Cicirello and Smith, "Modeling GA performance for
control parameter optimization", 2000.
"""
size = min(len(var1), len(var2))
# size = min(var1.size, var2.size)
p1, p2 = [0] * size, [0] * size
# Initialize the position of each indices in the individuals
for i in xrange(size):
p1[var1[i]] = i
p2[var2[i]] = i
for i in xrange(size):
if random.random() < prob:
# Keep track of the selected values
temp1 = var1[i]
temp2 = var2[i]
# Swap the matched value
var1[i], var1[p1[temp2]] = temp2, temp1
var2[i], var2[p2[temp1]] = temp1, temp2
# Position bookkeeping
p1[temp1], p1[temp2] = p1[temp2], p1[temp1]
p2[temp1], p2[temp2] = p2[temp2], p2[temp1]
return var1, var2