/usr/lib/python2.7/dist-packages/deap/tools/mutation.py is in python-deap 1.0.1-4.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | from __future__ import division
import math
import random
from itertools import repeat
from collections import Sequence
######################################
# GA Mutations #
######################################
def mutGaussian(individual, mu, sigma, indpb):
"""This function applies a gaussian mutation of mean *mu* and standard
deviation *sigma* on the input individual. This mutation expects a
:term:`sequence` individual composed of real valued attributes.
The *indpb* argument is the probability of each attribute to be mutated.
:param individual: Individual to be mutated.
:param mu: Mean or :term:`python:sequence` of means for the
gaussian addition mutation.
:param sigma: Standard deviation or :term:`python:sequence` of
standard deviations for the gaussian addition mutation.
:param indpb: Independent probability for each attribute to be mutated.
:returns: A tuple of one individual.
This function uses the :func:`~random.random` and :func:`~random.gauss`
functions from the python base :mod:`random` module.
"""
size = len(individual)
if not isinstance(mu, Sequence):
mu = repeat(mu, size)
elif len(mu) < size:
raise IndexError("mu must be at least the size of individual: %d < %d" % (len(mu), size))
if not isinstance(sigma, Sequence):
sigma = repeat(sigma, size)
elif len(sigma) < size:
raise IndexError("sigma must be at least the size of individual: %d < %d" % (len(sigma), size))
for i, m, s in zip(xrange(size), mu, sigma):
if random.random() < indpb:
individual[i] += random.gauss(m, s)
return individual,
def mutPolynomialBounded(individual, eta, low, up, indpb):
"""Polynomial mutation as implemented in original NSGA-II algorithm in
C by Deb.
:param individual: :term:`Sequence <sequence>` individual to be mutated.
:param eta: Crowding degree of the mutation. A high eta will produce
a mutant resembling its parent, while a small eta will
produce a solution much more different.
:param low: A value or a :term:`python:sequence` of values that
is the lower bound of the search space.
:param up: A value or a :term:`python:sequence` of values that
is the upper bound of the search space.
:returns: A tuple of one individual.
"""
size = len(individual)
if not isinstance(low, Sequence):
low = repeat(low, size)
elif len(low) < size:
raise IndexError("low must be at least the size of individual: %d < %d" % (len(low), size))
if not isinstance(up, Sequence):
up = repeat(up, size)
elif len(up) < size:
raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size))
for i, xl, xu in zip(xrange(size), low, up):
if random.random() <= indpb:
x = individual[i]
delta_1 = (x - xl) / (xu - xl)
delta_2 = (xu - x) / (xu - xl)
rand = random.random()
mut_pow = 1.0 / (eta + 1.)
if rand < 0.5:
xy = 1.0 - delta_1
val = 2.0 * rand + (1.0 - 2.0 * rand) * xy**(eta + 1)
delta_q = val**mut_pow - 1.0
else:
xy = 1.0 - delta_2
val = 2.0 * (1.0 - rand) + 2.0 * (rand - 0.5) * xy**(eta + 1)
delta_q = 1.0 - val**mut_pow
x = x + delta_q * (xu - xl)
x = min(max(x, xl), xu)
individual[i] = x
return individual,
def mutShuffleIndexes(individual, indpb):
"""Shuffle the attributes of the input individual and return the mutant.
The *individual* is expected to be a :term:`sequence`. The *indpb* argument is the
probability of each attribute to be moved. Usually this mutation is applied on
vector of indices.
:param individual: Individual to be mutated.
:param indpb: Independent probability for each attribute to be exchanged to
another position.
:returns: A tuple of one individual.
This function uses the :func:`~random.random` and :func:`~random.randint`
functions from the python base :mod:`random` module.
"""
size = len(individual)
for i in xrange(size):
if random.random() < indpb:
swap_indx = random.randint(0, size - 2)
if swap_indx >= i:
swap_indx += 1
individual[i], individual[swap_indx] = \
individual[swap_indx], individual[i]
return individual,
def mutFlipBit(individual, indpb):
"""Flip the value of the attributes of the input individual and return the
mutant. The *individual* is expected to be a :term:`sequence` and the values of the
attributes shall stay valid after the ``not`` operator is called on them.
The *indpb* argument is the probability of each attribute to be
flipped. This mutation is usually applied on boolean individuals.
:param individual: Individual to be mutated.
:param indpb: Independent probability for each attribute to be flipped.
:returns: A tuple of one individual.
This function uses the :func:`~random.random` function from the python base
:mod:`random` module.
"""
for i in xrange(len(individual)):
if random.random() < indpb:
individual[i] = type(individual[i])(not individual[i])
return individual,
def mutUniformInt(individual, low, up, indpb):
"""Mutate an individual by replacing attributes, with probability *indpb*,
by a integer uniformly drawn between *low* and *up* inclusively.
:param individual: :term:`Sequence <sequence>` individual to be mutated.
:param low: The lower bound or a :term:`python:sequence` of
of lower bounds of the range from wich to draw the new
integer.
:param up: The upper bound or a :term:`python:sequence` of
of upper bounds of the range from wich to draw the new
integer.
:param indpb: Independent probability for each attribute to be mutated.
:returns: A tuple of one individual.
"""
size = len(individual)
if not isinstance(low, Sequence):
low = repeat(low, size)
elif len(low) < size:
raise IndexError("low must be at least the size of individual: %d < %d" % (len(low), size))
if not isinstance(up, Sequence):
up = repeat(up, size)
elif len(up) < size:
raise IndexError("up must be at least the size of individual: %d < %d" % (len(up), size))
for i, xl, xu in zip(xrange(size), low, up):
if random.random() < indpb:
individual[i] = random.randint(xl, xu)
return individual,
######################################
# ES Mutations #
######################################
def mutESLogNormal(individual, c, indpb):
"""Mutate an evolution strategy according to its :attr:`strategy`
attribute as described in [Beyer2002]_. First the strategy is mutated
according to an extended log normal rule, :math:`\\boldsymbol{\sigma}_t =
\\exp(\\tau_0 \mathcal{N}_0(0, 1)) \\left[ \\sigma_{t-1, 1}\\exp(\\tau
\mathcal{N}_1(0, 1)), \ldots, \\sigma_{t-1, n} \\exp(\\tau
\mathcal{N}_n(0, 1))\\right]`, with :math:`\\tau_0 =
\\frac{c}{\\sqrt{2n}}` and :math:`\\tau = \\frac{c}{\\sqrt{2\\sqrt{n}}}`,
the the individual is mutated by a normal distribution of mean 0 and
standard deviation of :math:`\\boldsymbol{\sigma}_{t}` (its current
strategy) then . A recommended choice is ``c=1`` when using a :math:`(10,
100)` evolution strategy [Beyer2002]_ [Schwefel1995]_.
:param individual: :term:`Sequence <sequence>` individual to be mutated.
:param c: The learning parameter.
:param indpb: Independent probability for each attribute to be mutated.
:returns: A tuple of one individual.
.. [Beyer2002] Beyer and Schwefel, 2002, Evolution strategies - A
Comprehensive Introduction
.. [Schwefel1995] Schwefel, 1995, Evolution and Optimum Seeking.
Wiley, New York, NY
"""
size = len(individual)
t = c / math.sqrt(2. * math.sqrt(size))
t0 = c / math.sqrt(2. * size)
n = random.gauss(0, 1)
t0_n = t0 * n
for indx in xrange(size):
if random.random() < indpb:
individual.strategy[indx] *= math.exp(t0_n + t * random.gauss(0, 1))
individual[indx] += individual.strategy[indx] * random.gauss(0, 1)
return individual,
__all__ = ['mutGaussian', 'mutPolynomialBounded', 'mutShuffleIndexes',
'mutFlipBit', 'mutUniformInt', 'mutESLogNormal']
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