def GTreeGPMutatorOperation(genome, **args):
""" The mutator of GTreeGP, Operation Mutator
.. versionadded:: 0.6
The *GTreeGPMutatorOperation* function
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
ga_engine = args["ga_engine"]
gp_terminals = ga_engine.getParam("gp_terminals")
assert gp_terminals is not None
gp_function_set = ga_engine.getParam("gp_function_set")
assert gp_function_set is not None
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
rand_node = genome.getRandomNode()
assert rand_node is not None
if rand_node.getType() == Consts.nodeType["TERMINAL"]:
term_operator = rand_choice(gp_terminals)
else:
op_len = gp_function_set[rand_node.getData()]
fun_candidates = []
for o, l in gp_function_set.items():
if l == op_len:
fun_candidates.append(o)
if len(fun_candidates) <= 0:
continue
term_operator = rand_choice(fun_candidates)
rand_node.setData(term_operator)
else:
for it in xrange(int(round(mutations))):
rand_node = genome.getRandomNode()
assert rand_node is not None
if rand_node.getType() == Consts.nodeType["TERMINAL"]:
term_operator = rand_choice(gp_terminals)
else:
op_len = gp_function_set[rand_node.getData()]
fun_candidates = []
for o, l in gp_function_set.items():
if l == op_len:
fun_candidates.append(o)
if len(fun_candidates) <= 0:
continue
term_operator = rand_choice(fun_candidates)
rand_node.setData(term_operator)
return int(mutations)
def G2DListMutatorSwap(genome, **args):
""" The mutator of G1DList, Swap Mutator
.. note:: this mutator is :term:`Data Type Independent`
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(height):
for j in xrange(width):
if Util.randomFlipCoin(args["pmut"]):
index_b = (rand_randint(0, height - 1),
rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeList, (i, j), index_b)
mutations += 1
else:
for it in xrange(int(round(mutations))):
index_a = (rand_randint(0, height - 1), rand_randint(0, width - 1))
index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeList, index_a, index_b)
return int(mutations)
def GTreeGPInitializator(genome, **args):
"""This initializator accepts the follow parameters:
*max_depth*
The max depth of the tree
*method*
The method, accepts "grow", "full" or "ramped"
.. versionadded:: 0.6
The *GTreeGPInitializator* function.
"""
max_depth = genome.getParam("max_depth", 5)
method = genome.getParam("method", "grow")
ga_engine = args["ga_engine"]
if method == "grow":
root = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth)
elif method == "full":
root = GTree.buildGTreeGPFull(ga_engine, 0, max_depth)
elif method == "ramped":
if Util.randomFlipCoin(0.5):
root = GTree.buildGTreeGPFull(ga_engine, 0, max_depth)
else:
root = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth)
else:
Util.raiseException("Unknown tree initialization method [%s] !" % method)
genome.setRoot(root)
genome.processNodes()
assert genome.getHeight() <= max_depth
def G2DListMutatorIntegerRange(genome, **args):
""" Simple integer range mutator for G2DList
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
range_min = genome.getParam("rangemin", Consts.CDefRangeMin)
range_max = genome.getParam("rangemax", Consts.CDefRangeMax)
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
random_int = rand_randint(range_min, range_max)
genome.setItem(i, j, random_int)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
random_int = rand_randint(range_min, range_max)
genome.setItem(which_y, which_x, random_int)
return int(mutations)
def G2DBinaryStringXUniform(genome, **args):
""" The G2DBinaryString Uniform Crossover
.. versionadded:: 0.6
The *G2DBinaryStringXUniform* function
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
sister = gMom.clone()
brother = gDad.clone()
sister.resetStats()
brother.resetStats()
h, w = gMom.getSize()
for i in xrange(h):
for j in xrange(w):
if Util.randomFlipCoin(Consts.CDefG2DBinaryStringUniformProb):
temp = sister.getItem(i, j)
sister.setItem(i, j, brother.getItem(i, j))
brother.setItem(i, j, temp)
return (sister, brother)
def G1DBinaryStringMutatorFlip(genome, **args):
""" The classical flip mutator for binary strings """
if args["pmut"] <= 0.0:
return 0
stringLength = len(genome)
mutations = args["pmut"] * (stringLength)
if mutations < 1.0:
mutations = 0
for it in xrange(stringLength):
if Util.randomFlipCoin(args["pmut"]):
if genome[it] == 0:
genome[it] = 1
else:
genome[it] = 0
mutations += 1
else:
for it in xrange(int(round(mutations))):
which = rand_randint(0, stringLength - 1)
if genome[which] == 0:
genome[which] = 1
else:
genome[which] = 0
return int(mutations)
def G2DListMutatorSwap(genome, **args):
""" The mutator of G1DList, Swap Mutator
.. note:: this mutator is :term:`Data Type Independent`
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(height):
for j in xrange(width):
if Util.randomFlipCoin(args["pmut"]):
index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeList, (i, j), index_b)
mutations += 1
else:
for it in xrange(int(round(mutations))):
index_a = (rand_randint(0, height - 1), rand_randint(0, width - 1))
index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeList, index_a, index_b)
return int(mutations)
def G2DListMutatorIntegerRange(genome, **args):
""" Simple integer range mutator for G2DList
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
range_min = genome.getParam("rangemin", Consts.CDefRangeMin)
range_max = genome.getParam("rangemax", Consts.CDefRangeMax)
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
random_int = rand_randint(range_min, range_max)
genome.setItem(i, j, random_int)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
random_int = rand_randint(range_min, range_max)
genome.setItem(which_y, which_x, random_int)
return int(mutations)
def G1DListMutatorAllele(genome, **args):
""" The mutator of G1DList, Allele Mutator
To use this mutator, you must specify the *allele* genome parameter with the
:class:`GAllele.GAlleles` instance.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * listSize
allele = genome.getParam("allele", None)
if allele is None:
Util.raiseException("to use the G1DListMutatorAllele, you must specify the 'allele' parameter", TypeError)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
new_val = allele[it].getRandomAllele()
genome[it] = new_val
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
new_val = allele[which_gene].getRandomAllele()
genome[which_gene] = new_val
return int(mutations)
def G1DBinaryStringMutatorFlip(genome, **args):
""" The classical flip mutator for binary strings """
if args["pmut"] <= 0.0:
return 0
stringLength = len(genome)
mutations = args["pmut"] * (stringLength)
if mutations < 1.0:
mutations = 0
for it in xrange(stringLength):
if Util.randomFlipCoin(args["pmut"]):
if genome[it] == 0:
genome[it] = 1
else:
genome[it] = 0
mutations += 1
else:
for it in xrange(int(round(mutations))):
which = rand_randint(0, stringLength - 1)
if genome[which] == 0:
genome[which] = 1
else:
genome[which] = 0
return int(mutations)
def Reshuffle(genome, **args):
"""Reshuffle the order of the shapes"""
if Util.randomFlipCoin(args['pmut']):
shuffle(genome.shapes)
return 1
else:
return 0
def googleQueryMutator(genome, **args):
""" The mutator of GoogleQueryChromosome
"""
print "Mutating genome", genome
if args["pmut"] <= 0.0: return 0
# height, width = genome.getParam('seqlength')
height = 2
width = genome.getParam('seqlength')
elements = height * width
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(height):
for j in xrange(width):
if Util.randomFlipCoin(args["pmut"]):
index_b = (random.randint(0, height - 1),
random.randint(0, width - 1))
list2DSwapElement(genome.genomeList, (i, j), index_b)
mutations += 1
else:
for it in xrange(int(round(mutations))):
index_a = (random.randint(0, height - 1),
random.randint(0, width - 1))
index_b = (random.randint(0, height - 1),
random.randint(0, width - 1))
Util.list2DSwapElement(genome.genomeList, index_a, index_b)
return int(mutations)
def G1DListMutatorSIM(genome, **args):
""" The mutator of G1DList, Simple Inversion Mutation
.. note:: this mutator is :term:`Data Type Independent`
"""
mutations = 0
if args["pmut"] <= 0.0:
return 0
cuts = [rand_randint(0, len(genome)), rand_randint(0, len(genome))]
if cuts[0] > cuts[1]:
Util.listSwapElement(cuts, 0, 1)
if (cuts[1] - cuts[0]) <= 0:
cuts[1] = rand_randint(cuts[0], len(genome))
if Util.randomFlipCoin(args["pmut"]):
part = genome[cuts[0]:cuts[1]]
if len(part) == 0:
return 0
part.reverse()
genome[cuts[0]:cuts[1]] = part
mutations += 1
return mutations
def GTreeMutatorRealRange(genome, **args):
""" The mutator of GTree, Real Range Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
.. versionadded:: 0.6
The *GTreeMutatorRealRange* function
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
range_min = genome.getParam("rangemin", Consts.CDefRangeMin)
range_max = genome.getParam("rangemax", Consts.CDefRangeMax)
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
rand_node = genome.getRandomNode()
random_real = rand_uniform(range_min, range_max)
rand_node.setData(random_real)
else:
for it in xrange(int(round(mutations))):
rand_node = genome.getRandomNode()
random_real = rand_uniform(range_min, range_max)
rand_node.setData(random_real)
return int(mutations)
def GTreeMutatorRealGaussian(genome, **args):
""" A gaussian mutator for GTree of Real numbers
Accepts the *rangemin* and *rangemax* genome parameters, both optional. Also
accepts the parameter *gauss_mu* and the *gauss_sigma* which respectively
represents the mean and the std. dev. of the random distribution.
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
mu = genome.getParam("gauss_mu", Consts.CDefG1DListMutRealMU)
sigma = genome.getParam("gauss_sigma", Consts.CDefG1DListMutRealSIGMA)
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
rand_node = genome.getRandomNode()
final_value = rand_node.getData() + rand_gauss(mu, sigma)
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
rand_node.setData(final_value)
else:
for it in xrange(int(round(mutations))):
rand_node = genome.getRandomNode()
final_value = rand_node.getData() + rand_gauss(mu, sigma)
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
rand_node.setData(final_value)
return int(mutations)
def GTreeMutatorSwap(genome, **args):
""" The mutator of GTree, Swap Mutator
.. versionadded:: 0.6
The *GTreeMutatorSwap* function
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
nodeOne = genome.getRandomNode()
nodeTwo = genome.getRandomNode()
nodeOne.swapNodeData(nodeTwo)
else:
for it in xrange(int(round(mutations))):
nodeOne = genome.getRandomNode()
nodeTwo = genome.getRandomNode()
nodeOne.swapNodeData(nodeTwo)
return int(mutations)
def G2DBinaryStringMutatorSwap(genome, **args):
""" The mutator of G2DBinaryString, Swap Mutator
.. versionadded:: 0.6
The *G2DBinaryStringMutatorSwap* function
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(height):
for j in xrange(width):
if Util.randomFlipCoin(args["pmut"]):
index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeString, (i, j), index_b)
mutations += 1
else:
for it in xrange(int(round(mutations))):
index_a = (rand_randint(0, height - 1), rand_randint(0, width - 1))
index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeString, index_a, index_b)
return int(mutations)
def proposed_distribution_crossover(genome, **args):
g_mom = args["mom"]
g_dad = args["dad"]
if not isinstance(g_mom, ProposedDistributionGenome):
raise ValueError("this crossover is for ProposedDistributionGenome")
if not isinstance(g_dad, ProposedDistributionGenome):
raise ValueError("this crossover is for ProposedDistributionGenome")
mom_clone = g_mom.clone()
dad_clone = g_dad.clone()
mom_clone.resetStats()
dad_clone.resetStats()
# print "Crossover old: ", mom_clone.proposed_distribution, ", ", dad_clone.proposed_distribution
h, w = g_mom.num_populated_areas, g_mom.num_power_plants
for i in xrange(h):
for j in xrange(w):
if Util.randomFlipCoin(Consts.CDefG2DBinaryStringUniformProb):
temp = mom_clone.proposed_distribution[i][j]
mom_clone.proposed_distribution[i][
j] = dad_clone.proposed_distribution[i][j]
dad_clone.proposed_distribution[i][j] = temp
# print "Crossover new: ", mom_clone.proposed_distribution, ", ", dad_clone.proposed_distribution
return mom_clone, dad_clone
def GTreeGPMutatorOperation(genome, **args):
""" The mutator of GTreeGP, Operation Mutator
.. versionadded:: 0.6
The *GTreeGPMutatorOperation* function
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
ga_engine = args["ga_engine"]
gp_terminals = ga_engine.getParam("gp_terminals")
assert gp_terminals is not None
gp_function_set = ga_engine.getParam("gp_function_set")
assert gp_function_set is not None
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
rand_node = genome.getRandomNode()
assert rand_node is not None
if rand_node.getType() == Consts.nodeType["TERMINAL"]:
term_operator = rand_choice(gp_terminals)
else:
op_len = gp_function_set[rand_node.getData()]
fun_candidates = []
for o, l in gp_function_set.items():
if l == op_len:
fun_candidates.append(o)
if len(fun_candidates) <= 0:
continue
term_operator = rand_choice(fun_candidates)
rand_node.setData(term_operator)
else:
for it in xrange(int(round(mutations))):
rand_node = genome.getRandomNode()
assert rand_node is not None
if rand_node.getType() == Consts.nodeType["TERMINAL"]:
term_operator = rand_choice(gp_terminals)
else:
op_len = gp_function_set[rand_node.getData()]
fun_candidates = []
for o, l in gp_function_set.items():
if l == op_len:
fun_candidates.append(o)
if len(fun_candidates) <= 0:
continue
term_operator = rand_choice(fun_candidates)
rand_node.setData(term_operator)
return int(mutations)
def GTreeMutatorIntegerRange(genome, **args):
""" The mutator of GTree, Integer Range Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
.. versionadded:: 0.6
The *GTreeMutatorIntegerRange* function
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
range_min = genome.getParam("rangemin", Consts.CDefRangeMin)
range_max = genome.getParam("rangemax", Consts.CDefRangeMax)
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
rand_node = genome.getRandomNode()
random_int = rand_randint(range_min, range_max)
rand_node.setData(random_int)
else:
for it in xrange(int(round(mutations))):
rand_node = genome.getRandomNode()
random_int = rand_randint(range_min, range_max)
rand_node.setData(random_int)
return int(mutations)
def G1DListMutatorRealRange(genome, **args):
""" Simple real range mutator for G1DList
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
genome[it] = rand_uniform(genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
genome[which_gene] = rand_uniform(genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
return int(mutations)
def G2DBinaryStringMutatorSwap(genome, **args):
""" The mutator of G2DBinaryString, Swap Mutator
.. versionadded:: 0.6
The *G2DBinaryStringMutatorSwap* function
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(height):
for j in xrange(width):
if Util.randomFlipCoin(args["pmut"]):
index_b = (rand_randint(0, height - 1),
rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeString, (i, j),
index_b)
mutations += 1
else:
for it in xrange(int(round(mutations))):
index_a = (rand_randint(0, height - 1), rand_randint(0, width - 1))
index_b = (rand_randint(0, height - 1), rand_randint(0, width - 1))
Util.list2DSwapElement(genome.genomeString, index_a, index_b)
return int(mutations)
def G1DListMutatorIntegerBinary(genome, **args):
""" The mutator of G1DList, the binary mutator
This mutator will random change the 0 and 1 elements of the 1D List.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
if genome[it] == 0:
genome[it] = 1
elif genome[it] == 1:
genome[it] = 0
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
if genome[which_gene] == 0:
genome[which_gene] = 1
elif genome[which_gene] == 1:
genome[which_gene] = 0
return int(mutations)
def G2DBinaryStringXUniform(genome, **args):
""" The G2DBinaryString Uniform Crossover
.. versionadded:: 0.6
The *G2DBinaryStringXUniform* function
"""
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
sister = gMom.clone()
brother = gDad.clone()
sister.resetStats()
brother.resetStats()
h, w = gMom.getSize()
for i in xrange(h):
for j in xrange(w):
if Util.randomFlipCoin(Consts.CDefG2DBinaryStringUniformProb):
temp = sister.getItem(i, j)
sister.setItem(i, j, brother.getItem(i, j))
brother.setItem(i, j, temp)
return (sister, brother)
def proposed_distribution_mutator(genome, **args):
if not isinstance(genome, ProposedDistributionGenome):
raise ValueError(
"this mutator can only work with genomes of type ProposedDistributionGenome"
)
if args["pmut"] <= 0.0:
return 0
height, width = genome.num_populated_areas, genome.num_power_plants
elements = height * width
mutations = args["pmut"] * elements
range_min = 0
range_max = 100
if mutations < 1.0:
mutations = 0
for i in xrange(height):
for j in xrange(width):
if Util.randomFlipCoin(args["pmut"]):
random_int = randint(range_min, range_max)
genome.proposed_distribution[i][j] = random_int
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = randint(0, width - 1)
which_y = randint(0, height - 1)
random_int = randint(range_min, range_max)
genome.proposed_distribution[which_y][which_x] = random_int
return int(mutations)
def G1DListMutatorRealRange(genome, **args):
""" Simple real range mutator for G1DList
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
genome[it] = rand_uniform(
genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
genome[which_gene] = rand_uniform(
genome.getParam("rangemin", Consts.CDefRangeMin),
genome.getParam("rangemax", Consts.CDefRangeMax))
return int(mutations)
def G1DListMutatorIntegerBinary(genome, **args):
""" The mutator of G1DList, the binary mutator
This mutator will random change the 0 and 1 elements of the 1D List.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
if genome[it] == 0:
genome[it] = 1
elif genome[it] == 1:
genome[it] = 0
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
if genome[which_gene] == 0:
genome[which_gene] = 1
elif genome[which_gene] == 1:
genome[which_gene] = 0
return int(mutations)
def G1DListMutatorAllele(genome, **args):
""" The mutator of G1DList, Allele Mutator
To use this mutator, you must specify the *allele* genome parameter with the
:class:`GAllele.GAlleles` instance.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * listSize
allele = genome.getParam("allele", None)
if allele is None:
Util.raiseException(
"to use the G1DListMutatorAllele, you must specify the 'allele' parameter",
TypeError)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
new_val = allele[it].getRandomAllele()
genome[it] = new_val
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
new_val = allele[which_gene].getRandomAllele()
genome[which_gene] = new_val
return int(mutations)
def GTreeGPInitializator(genome, **args):
"""This initializator accepts the follow parameters:
*max_depth*
The max depth of the tree
*method*
The method, accepts "grow", "full" or "ramped"
.. versionadded:: 0.6
The *GTreeGPInitializator* function.
"""
max_depth = genome.getParam("max_depth", 5)
method = genome.getParam("method", "grow")
ga_engine = args["ga_engine"]
if method == "grow":
root = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth)
elif method == "full":
root = GTree.buildGTreeGPFull(ga_engine, 0, max_depth)
elif method == "ramped":
if Util.randomFlipCoin(0.5):
root = GTree.buildGTreeGPFull(ga_engine, 0, max_depth)
else:
root = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth)
else:
Util.raiseException("Unknown tree initialization method [%s] !" % method)
genome.setRoot(root)
genome.processNodes()
assert genome.getHeight() <= max_depth
def GTreeMutatorSwap(genome, **args):
""" The mutator of GTree, Swap Mutator
.. versionadded:: 0.6
The *GTreeMutatorSwap* function
"""
if args["pmut"] <= 0.0:
return 0
elements = len(genome)
mutations = args["pmut"] * elements
if mutations < 1.0:
mutations = 0
for i in xrange(len(genome)):
if Util.randomFlipCoin(args["pmut"]):
mutations += 1
nodeOne = genome.getRandomNode()
nodeTwo = genome.getRandomNode()
nodeOne.swapNodeData(nodeTwo)
else:
for it in xrange(int(round(mutations))):
nodeOne = genome.getRandomNode()
nodeTwo = genome.getRandomNode()
nodeOne.swapNodeData(nodeTwo)
return int(mutations)
def G1DListMutatorSIM(genome, **args):
""" The mutator of G1DList, Simple Inversion Mutation
.. note:: this mutator is :term:`Data Type Independent`
"""
mutations = 0
if args["pmut"] <= 0.0:
return 0
cuts = [rand_randint(0, len(genome)), rand_randint(0, len(genome))]
if cuts[0] > cuts[1]:
Util.listSwapElement(cuts, 0, 1)
if (cuts[1] - cuts[0]) <= 0:
cuts[1] = rand_randint(cuts[0], len(genome))
if Util.randomFlipCoin(args["pmut"]):
part = genome[cuts[0]:cuts[1]]
if len(part) == 0:
return 0
part.reverse()
genome[cuts[0]:cuts[1]] = part
mutations += 1
return mutations
def AdjustBackground(genome, **args):
"""Adjust the background color"""
if Util.randomFlipCoin(args['pmut']):
bg = genome.bg
genome.bg = mutatecolor(bg)
return 1
else:
return 0
def AddShape(genome, **args):
""" Add a random shape"""
if Util.randomFlipCoin(args['pmut']):
t = randshape(genome)
genome.shapes.append(t)
return 1
else:
return 0
def Transpose(genome, **args):
"""Transpose two shapes"""
if Util.randomFlipCoin(args['pmut']) and len(genome.shapes) >= 2:
i,j = sample(xrange(len(genome.shapes)),2)
genome.shapes[i], genome.shapes[j] = genome.shapes[j], genome.shapes[i]
return 1
else:
return 0
def Crawl(genome, **args):
if len(genome.shapes) == 0: return 0
mut_count = 0
if Util.randomFlipCoin(args['pmut']):
for i in range(len(genome.shapes)):
genome.shapes[i] = genome.shapes[i].mutate(genome)
mut_count += 1
return mut_count
def G2DListMutatorRealGaussian(genome, **args):
""" A gaussian mutator for G2DList of Real
Accepts the *rangemin* and *rangemax* genome parameters, both optional. Also
accepts the parameter *gauss_mu* and the *gauss_sigma* which respectively
represents the mean and the std. dev. of the random distribution.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG2DListMutRealMU
if sigma is None:
sigma = Consts.CDefG2DListMutRealSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] + rand_gauss(mu, sigma)
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = genome[which_y][which_x] + rand_gauss(mu, sigma)
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def MutateShape(genome, **args):
""" Move the vertexes of a random polygon"""
if len(genome.shapes) == 0: return 0
mut_count = 0
for i in range(len(genome.shapes)):
if Util.randomFlipCoin(args['pmut']):
genome.shapes[i] = genome.shapes[i].mutate(genome)
mut_count += 1
return mut_count
def RemoveShape(genome, **args):
""" Remove a random shape"""
if len(genome.shapes) == 0: return 0
if Util.randomFlipCoin(args['pmut']):
index = choice(xrange(len(genome.shapes)))
del genome.shapes[index]
return 1
else:
return 0
def ChangeShapeColor(genome, **args):
""" Modify shape color at random """
if len(genome.shapes) == 0: return 0
mut_count = 0
for i in range(len(genome.shapes)):
if Util.randomFlipCoin(args['pmut']):
t = genome.shapes[i].copy()
t.color = mutatecolor(t.color)
genome.shapes[i] = t
return mut_count
def G2DListMutatorIntegerGaussianGradient(genome, **args):
""" A gaussian mutator for G2DList of Integers
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = int(genome[i][j] *
abs(rand_gauss(mu, sigma)))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = int(genome[which_y][which_x] *
abs(rand_gauss(mu, sigma)))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def mutate_subset(subset):
mutations = 0
for idx in xrange(len(subset)):
if Util.randomFlipCoin(args["pmut"]):
Util.listSwapElement(
genome, subset[idx],
subset[rand_randint(0,
len(subset) - 1)])
mutations += 1
return mutations
def G1DListMutatorRealGaussianGradient(genome, **args):
""" The mutator of G1DList, Gaussian Gradient Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional. The
random distribution is set with mu=1.0 and std=0.0333
The difference between this routine and the normal Gaussian Real is that the
other function generates a gaussian value and adds it to the value. If the
mu is 0, and the std is 1, a typical value could be 1.8 or -0.5. These small
values are fine if your range is 0-10, but if your range is much larger, like
0-100,000, a relative gradient makes sense.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[it] * abs(rand_gauss(mu, sigma))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[it] = final_value
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = genome[which_gene] * abs(rand_gauss(mu, sigma))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[which_gene] = final_value
return int(mutations)
def G2DListMutatorRealGaussianGradient(genome, **args):
""" A gaussian gradient mutator for G2DList of Real
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
The difference is that this multiplies the gene by gauss(1.0, 0.0333), allowing
for a smooth gradient drift about the value.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] * abs(rand_gauss(mu, sigma))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = genome[which_y][which_x] * abs(rand_gauss(mu, sigma))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def attack_crossover(genome, **args):
g_mom = args["mom"]
g_dad = args["dad"]
sister = g_mom.clone()
brother = g_dad.clone()
sister.resetStats()
brother.resetStats()
for i in xrange(len(g_mom)):
if Util.randomFlipCoin(0.5):
sister[i], brother[i] = brother[i], sister[i]
return sister, brother
def attack_crossover(genome, **args):
g_mom = args["mom"]
g_dad = args["dad"]
sister = g_mom.clone()
brother = g_dad.clone()
sister.resetStats()
brother.resetStats()
for i in xrange(len(g_mom)):
if Util.randomFlipCoin(0.5):
sister[i], brother[i] = brother[i], sister[i]
return sister, brother
def G2DListMutatorRealGaussian(genome, **args):
""" A gaussian mutator for G2DList of Real
Accepts the *rangemin* and *rangemax* genome parameters, both optional. Also
accepts the parameter *gauss_mu* and the *gauss_sigma* which respectively
represents the mean and the std. dev. of the random distribution.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG2DListMutRealMU
if sigma is None:
sigma = Consts.CDefG2DListMutRealSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] + rand_gauss(mu, sigma)
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = genome[which_y][which_x] + rand_gauss(mu, sigma)
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def GTreeGPMutatorSubtree(genome, **args):
""" The mutator of GTreeGP, Subtree Mutator
This mutator will recreate random subtree of the tree using the grow algorithm.
.. versionadded:: 0.6
The *GTreeGPMutatorSubtree* function
"""
if args["pmut"] <= 0.0:
return 0
ga_engine = args["ga_engine"]
max_depth = genome.getParam("max_depth", None)
mutations = 0
if max_depth is None:
Util.raiseException(
"You must specify the max_depth genome parameter !", ValueError)
if max_depth < 0:
Util.raiseException(
"The max_depth must be >= 1, if you want to use GTreeGPMutatorSubtree crossover !",
ValueError)
branch_list = genome.nodes_branch
elements = len(branch_list)
for i in xrange(elements):
node = branch_list[i]
assert node is not None
if Util.randomFlipCoin(args["pmut"]):
depth = genome.getNodeDepth(node)
mutations += 1
root_subtree = GTree.buildGTreeGPGrow(ga_engine, 0,
max_depth - depth)
node_parent = node.getParent()
if node_parent is None:
genome.setRoot(root_subtree)
genome.processNodes()
return mutations
else:
root_subtree.setParent(node_parent)
node_parent.replaceChild(node, root_subtree)
genome.processNodes()
return int(mutations)
def G1DListMutatorRealGaussian(genome, **args):
""" The mutator of G1DList, Gaussian Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional. Also
accepts the parameter *gauss_mu* and the *gauss_sigma* which respectively
represents the mean and the std. dev. of the random distribution.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG1DListMutRealMU
if sigma is None:
sigma = Consts.CDefG1DListMutRealSIGMA
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[it] + rand_gauss(mu, sigma)
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[it] = final_value
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = genome[which_gene] + rand_gauss(mu, sigma)
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[which_gene] = final_value
return int(mutations)
def G1DListMutatorIntegerGaussianGradient(genome, **args):
""" A gaussian mutator for G1DList of Integers
Accepts the *rangemin* and *rangemax* genome parameters, both optional. The
random distribution is set with mu=1.0 and std=0.0333
Same as IntegerGaussian, except that this uses relative gradient rather than
absolute gaussian. A value is randomly generated about gauss(mu=1, sigma=.0333)
and multiplied by the gene to drift it up or down (depending on what side of
1 the random value falls on) and cast to integer
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
final_value = int(genome[it] * abs(rand_gauss(mu, sigma)))
final_value = min(
final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(
final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[it] = final_value
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = int(genome[which_gene] * abs(rand_gauss(mu, sigma)))
final_value = min(final_value,
genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value,
genome.getParam("rangemin", Consts.CDefRangeMin))
genome[which_gene] = final_value
return int(mutations)
def G1DListMutatorAlleleGaussian(genome, **arguments):
"""An allele-based mutator based on G1DListMutatorRealGaussian.
Accepts the parameter *gauss_mu* and the *gauss_sigma* which
respectively represents the mean and the std. dev. of the random
distribution.
"""
if arguments["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = arguments["pmut"] * listSize
mu = genome.getParam("gauss_mu")
sigma = genome.getParam("gauss_sigma")
if mu is None:
mu = Consts.CDefG1DListMutRealMU
if sigma is None:
sigma = Consts.CDefG1DListMutRealSIGMA
allele = genome.getParam("allele", None)
if allele is None:
Util.raiseException(
"to use this mutator, you must specify the 'allele' parameter",
TypeError)
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(arguments["pmut"]):
final_value = genome[it] + rand_gauss(mu, sigma)
assert len(allele[it].beginEnd
) == 1, "only single ranges are supported"
rangemin, rangemax = allele[it].beginEnd[0]
final_value = min(final_value, rangemax)
final_value = max(final_value, rangemin)
genome[it] = final_value
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = genome[which_gene] + rand_gauss(mu, sigma)
assert len(allele[which_gene].beginEnd
) == 1, "only single ranges are supported"
rangemin, rangemax = allele[which_gene].beginEnd[0]
final_value = min(final_value, rangemax)
final_value = max(final_value, rangemin)
genome[which_gene] = final_value
return int(mutations)
def G2DListMutatorIntegerGaussianGradient(genome, **args):
""" A gaussian mutator for G2DList of Integers
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = int(genome[i][j] * abs(rand_gauss(mu, sigma)))
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = int(genome[which_y][which_x] * abs(rand_gauss(mu, sigma)))
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def G1DListMutatorRealGaussianGradient(genome, **args):
""" The mutator of G1DList, Gaussian Gradient Mutator
Accepts the *rangemin* and *rangemax* genome parameters, both optional. The
random distribution is set with mu=1.0 and std=0.0333
The difference between this routine and the normal Gaussian Real is that the
other function generates a gaussian value and adds it to the value. If the
mu is 0, and the std is 1, a typical value could be 1.8 or -0.5. These small
values are fine if your range is 0-10, but if your range is much larger, like
0-100,000, a relative gradient makes sense.
This routine generates a gaussian value with mu=1.0 and std=0.0333 and then
the gene is multiplied by this value. This will cause the gene to drift
no matter how large it is.
"""
if args["pmut"] <= 0.0:
return 0
listSize = len(genome)
mutations = args["pmut"] * (listSize)
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for it in xrange(listSize):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[it] * abs(rand_gauss(mu, sigma))
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome[it] = final_value
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_randint(0, listSize - 1)
final_value = genome[which_gene] * abs(rand_gauss(mu, sigma))
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome[which_gene] = final_value
return int(mutations)
def GTreeGPMutatorSubtree(genome, **args):
""" The mutator of GTreeGP, Subtree Mutator
This mutator will recreate random subtree of the tree using the grow algorithm.
.. versionadded:: 0.6
The *GTreeGPMutatorSubtree* function
"""
if args["pmut"] <= 0.0:
return 0
ga_engine = args["ga_engine"]
max_depth = genome.getParam("max_depth", None)
mutations = 0
if max_depth is None:
Util.raiseException("You must specify the max_depth genome parameter !", ValueError)
if max_depth < 0:
Util.raiseException("The max_depth must be >= 1, if you want to use GTreeGPMutatorSubtree crossover !", ValueError)
branch_list = genome.nodes_branch
elements = len(branch_list)
for i in xrange(elements):
node = branch_list[i]
assert node is not None
if Util.randomFlipCoin(args["pmut"]):
depth = genome.getNodeDepth(node)
mutations += 1
root_subtree = GTree.buildGTreeGPGrow(ga_engine, 0, max_depth - depth)
node_parent = node.getParent()
if node_parent is None:
genome.setRoot(root_subtree)
genome.processNodes()
return mutations
else:
root_subtree.setParent(node_parent)
node_parent.replaceChild(node, root_subtree)
genome.processNodes()
return int(mutations)
def G2DListMutatorRealGaussianGradient(genome, **args):
""" A gaussian gradient mutator for G2DList of Real
Accepts the *rangemin* and *rangemax* genome parameters, both optional.
The difference is that this multiplies the gene by gauss(1.0, 0.0333), allowing
for a smooth gradient drift about the value.
"""
if args["pmut"] <= 0.0:
return 0
height, width = genome.getSize()
elements = height * width
mutations = args["pmut"] * elements
mu = Consts.CDefGaussianGradientMU
sigma = Consts.CDefGaussianGradientSIGMA
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
final_value = genome[i][j] * abs(rand_gauss(mu, sigma))
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(i, j, final_value)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getWidth() - 1)
which_y = rand_randint(0, genome.getHeight() - 1)
final_value = genome[which_y][which_x] * abs(rand_gauss(mu, sigma))
final_value = min(final_value, genome.getParam("rangemax", Consts.CDefRangeMax))
final_value = max(final_value, genome.getParam("rangemin", Consts.CDefRangeMin))
genome.setItem(which_y, which_x, final_value)
return int(mutations)
def attack_simple_mutator(genome, **args):
if args["pmut"] <= 0.0:
return 0
list_size = len(genome)
mutations = args["pmut"] * list_size
if mutations < 1.0:
mutations = 0
for it in xrange(list_size):
if Util.randomFlipCoin(args["pmut"]):
genome = rand_init(genome, it)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_init(0, list_size - 1)
genome = rand_init(genome, which_gene)
return mutations
def attack_simple_mutator(genome, **args):
if args["pmut"] <= 0.0:
return 0
list_size = len(genome)
mutations = args["pmut"] * list_size
if mutations < 1.0:
mutations = 0
for it in xrange(list_size):
if Util.randomFlipCoin(args["pmut"]):
genome = rand_init(genome, it)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_gene = rand_init(0, list_size - 1)
genome = rand_init(genome, which_gene)
return mutations
def G1DConnCrossoverWeights(genome, **args):
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
sister = gMom.clone()
brother = gDad.clone()
sister.resetStats()
brother.resetStats()
for i in xrange(len(gMom)):
if Util.randomFlipCoin(Consts.CDefG1DListCrossUniformProb):
temp = sister[i][2]
sister[i][2] = brother[i][2]
brother[i][2] = temp
return (sister, brother)
def GTreeInitializatorInteger(genome, **args):
""" Integer initialization function of GTree
This initializator accepts the *rangemin* and *rangemax* genome parameters.
It accepts the following parameters too:
*max_depth*
The max depth of the tree
*max_siblings*
The number of maximum siblings of an node
*method*
The method, accepts "grow", "full" or "ramped".
.. versionadded:: 0.6
The *GTreeInitializatorInteger* function.
"""
max_depth = genome.getParam("max_depth", 5)
max_siblings = genome.getParam("max_siblings", 2)
range_min = genome.getParam("rangemin", 0)
range_max = genome.getParam("rangemax", 100)
lambda_generator = lambda: rand_randint(range_min, range_max)
method = genome.getParam("method", "grow")
if method == "grow":
root = GTree.buildGTreeGrow(0, lambda_generator, max_siblings, max_depth)
elif method == "full":
root = GTree.buildGTreeFull(0, lambda_generator, max_siblings, max_depth)
elif method == "ramped":
if Util.randomFlipCoin(0.5):
root = GTree.buildGTreeGrow(0, lambda_generator, max_siblings, max_depth)
else:
root = GTree.buildGTreeFull(0, lambda_generator, max_siblings, max_depth)
else:
Util.raiseException("Unknown tree initialization method [%s] !" % method)
genome.setRoot(root)
genome.processNodes()
assert genome.getHeight() <= max_depth
def G2DListMutatorAllele(genome, **args):
""" The mutator of G2DList, Allele Mutator
To use this mutator, you must specify the *allele* genome parameter with the
:class:`GAllele.GAlleles` instance.
.. warning:: the :class:`GAllele.GAlleles` instance must have the homogeneous flag enabled
"""
if args["pmut"] <= 0.0:
return 0
listSize = genome.getHeight() * genome.getWidth() - 1
mutations = args["pmut"] * (listSize + 1)
allele = genome.getParam("allele", None)
if allele is None:
Util.raiseException(
"to use the G2DListMutatorAllele, you must specify the 'allele' parameter",
TypeError)
if not allele.homogeneous:
Util.raiseException(
"to use the G2DListMutatorAllele, the 'allele' must be homogeneous"
)
if mutations < 1.0:
mutations = 0
for i in xrange(genome.getHeight()):
for j in xrange(genome.getWidth()):
if Util.randomFlipCoin(args["pmut"]):
new_val = allele[0].getRandomAllele()
genome.setItem(i, j, new_val)
mutations += 1
else:
for it in xrange(int(round(mutations))):
which_x = rand_randint(0, genome.getHeight() - 1)
which_y = rand_randint(0, genome.getWidth() - 1)
new_val = allele[0].getRandomAllele()
genome.setItem(which_x, which_y, new_val)
return int(mutations)
def G1DBinaryStringXUniform(genome, **args):
""" The G1DList Uniform Crossover """
sister = None
brother = None
gMom = args["mom"]
gDad = args["dad"]
sister = gMom.clone()
brother = gDad.clone()
sister.resetStats()
brother.resetStats()
for i in xrange(len(gMom)):
if Util.randomFlipCoin(Consts.CDefG1DBinaryStringUniformProb):
temp = sister[i]
sister[i] = brother[i]
brother[i] = temp
return (sister, brother)
