/usr/lib/python3/dist-packages/deap/benchmarks/binary.py is in python3-deap 1.0.2.post2-2.
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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 | # This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public
# License along with DEAP. If not, see <http://www.gnu.org/licenses/>.
import math
def bin2float(min_, max_, nbits):
"""Convert a binary array into an array of float where each
float is composed of *nbits* and is between *min_* and *max_*
and return the result of the decorated function.
.. note::
This decorator requires the first argument of
the evaluation function to be named *individual*.
"""
def wrap(function):
def wrapped_function(individual, *args, **kargs):
nelem = len(individual)/nbits
decoded = [0] * nelem
for i in range(nelem):
gene = int("".join(map(str, individual[i*nbits:i*nbits+nbits])), 2)
div = 2**nbits - 1
temp = float(gene)/float(div)
decoded[i] = min_ + (temp * (max_ - min_))
return function(decoded, *args, **kargs)
return wrapped_function
return wrap
def trap(individual):
u = sum(individual)
k = len(individual)
if u == k:
return k
else:
return k - 1 - u
def inv_trap(individual):
u = sum(individual)
k = len(individual)
if u == 0:
return k
else:
return u - 1
def chuang_f1(individual):
"""Binary deceptive function from : Multivariate Multi-Model Approach for
Globally Multimodal Problems by Chung-Yao Chuang and Wen-Lian Hsu.
The function takes individual of 40+1 dimensions and has two global optima
in [1,1,...,1] and [0,0,...,0].
"""
total = 0
if individual[-1] == 0:
for i in range(0,len(individual)-1,4):
total += inv_trap(individual[i:i+4])
else:
for i in range(0,len(individual)-1,4):
total += trap(individual[i:i+4])
return total,
def chuang_f2(individual):
"""Binary deceptive function from : Multivariate Multi-Model Approach for
Globally Multimodal Problems by Chung-Yao Chuang and Wen-Lian Hsu.
The function takes individual of 40+1 dimensions and has four global optima
in [1,1,...,0,0], [0,0,...,1,1], [1,1,...,1] and [0,0,...,0].
"""
total = 0
if individual[-2] == 0 and individual[-1] == 0:
for i in range(0,len(individual)-2,8):
total += inv_trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8])
elif individual[-2] == 0 and individual[-1] == 1:
for i in range(0,len(individual)-2,8):
total += inv_trap(individual[i:i+4]) + trap(individual[i+4:i+8])
elif individual[-2] == 1 and individual[-1] == 0:
for i in range(0,len(individual)-2,8):
total += trap(individual[i:i+4]) + inv_trap(individual[i+4:i+8])
else:
for i in range(0,len(individual)-2,8):
total += trap(individual[i:i+4]) + trap(individual[i+4:i+8])
return total,
def chuang_f3(individual):
"""Binary deceptive function from : Multivariate Multi-Model Approach for
Globally Multimodal Problems by Chung-Yao Chuang and Wen-Lian Hsu.
The function takes individual of 40+1 dimensions and has two global optima
in [1,1,...,1] and [0,0,...,0].
"""
total = 0
if individual[-1] == 0:
for i in range(0,len(individual)-1,4):
total += inv_trap(individual[i:i+4])
else:
for i in range(2,len(individual)-3,4):
total += inv_trap(individual[i:i+4])
total += trap(individual[-2:]+individual[:2])
return total,
# Royal Road Functions
def royal_road1(individual, order):
"""Royal Road Function R1 as presented by Melanie Mitchell in :
"An introduction to Genetic Algorithms".
"""
nelem = len(individual) / order
max_value = int(2**order - 1)
total = 0
for i in range(nelem):
value = int("".join(map(str, individual[i*order:i*order+order])), 2)
total += int(order) * int(value/max_value)
return total,
def royal_road2(individual, order):
"""Royal Road Function R2 as presented by Melanie Mitchell in :
"An introduction to Genetic Algorithms".
"""
total = 0
norder = order
while norder < order**2:
total += royal_road1(norder, individual)[0]
norder *= 2
return total,
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