def gale_4_WHERE(problem, population, configuration, values_to_be_passed):
"The Core method behind GALE"
# for pop in population:
# assert(pop.generation_number == 0), "Generation has to be 0"
# Compile population into table form used by WHERE
t = slurp(
[[x for x in row.decisionValues] + ["?" for y in problem.objectives]
for row in population],
problem.buildHeader().split(","))
# Initialize some parameters for WHERE
The.allowDomination = True
The.alpha = 1
for i, row in enumerate(t.rows):
row.evaluated = False
# Run WHERE
m = Moo(problem, t, len(t.rows), N=1).divide(minnie=rstop(t))
# Organizing
NDLeafs = m.nonPrunedLeaves() # The surviving non-dominated leafs
allLeafs = m.nonPrunedLeaves() + m.prunedLeaves() # All of the leafs
# After mutation: Check how many rows were actually evaluated
numEval = 0
for leaf in allLeafs:
for row in leaf.table.rows:
if row.evaluated:
numEval += 1
return NDLeafs, numEval
def gale_64_WHERE(problem, population, configuration, values_to_be_passed):
"The Core method behind GALE"
# Compile population into table form used by WHERE
t = slurp([[x for x in row.decisionValues] + ["?" for y in problem.objectives] for row in population],
problem.buildHeader().split(","))
# Initialize some parameters for WHERE
The.allowDomination = True
The.alpha = 1
for i, row in enumerate(t.rows):
row.evaluated = False
# Run WHERE
m = Moo(problem, t, len(t.rows), N=1).divide(minnie=rstop(t))
# Organizing
NDLeafs = m.nonPrunedLeaves() # The surviving non-dominated leafs
allLeafs = m.nonPrunedLeaves() + m.prunedLeaves() # All of the leafs
# After mutation: Check how many rows were actually evaluated
numEval = 0
for leaf in allLeafs:
for row in leaf.table.rows:
if row.evaluated:
numEval += 1
return NDLeafs, numEval
def gale0WHERE(problem, population, configuration, values_to_be_passed):
"The Core method behind GALE"
# for pop in population:
# assert(pop.generation_number == 0), "Generation has to be 0"
# Compile population into table form used by WHERE
t = slurp([[x for x in row.decisionValues] + ["?" for y in problem.objectives] for row in population],
problem.buildHeader().split(","))
# Initialize some parameters for WHERE
The.allowDomination = True
The.alpha = 1
for i, row in enumerate(t.rows):
row.evaluated = False
# Run WHERE
m = Moo(problem, t, len(t.rows), N=1).divide(minnie=rstop(t))
print "Where done"
# Organizing
NDLeafs = m.nonPrunedLeaves() # The surviving non-dominated leafs
allLeafs = m.nonPrunedLeaves() + m.prunedLeaves() # All of the leafs
# After mutation: Check how many rows were actually evaluated
numEval = 0
for leaf in allLeafs:
for row in leaf.table.rows:
if row.evaluated:
numEval += 1
# After mutation; Convert back to JMOO Data Structures
population = []
for leaf in NDLeafs:
for row in leaf.table.rows:
if row.evaluated:
population.append(jmoo_individual(problem, [x for x in row.cells[:len(problem.decisions)]], 0,
[x for x in row.cells[len(problem.decisions):]]))
else:
indi = jmoo_individual(problem, [x for x in row.cells[:len(problem.decisions)]], 0, None)
indi.fitness.fitness = problem.evaluate(indi.decisionValues)
# print "> ", indi.fitness.fitness
population.append(indi)
numEval += 1
# median_values = []
# for i in xrange(len(problem.objectives)):
# temp_values = []
# for pop in population:
# temp_values.append(pop.fitness.fitness[i])
# median_values.append(median(temp_values))
#
# print median_values
print "number of evals: ", numEval
return population, numEval
def galeEWWHERE(problem, population, configuration, values_to_be_passed):
"The Core method behind GALE"
# for pop in population:
# assert(pop.generation_number == 0), "Generation has to be 0"
# Compile population into table form used by WHERE
t = slurp([[x for x in row.decisionValues] + ["?" for y in problem.objectives] for row in population],
problem.buildHeader().split(","))
# Initialize some parameters for WHERE
The.allowDomination = True
The.alpha = 1
for i, row in enumerate(t.rows):
row.evaluated = False
# Run WHERE
m = Moo(problem, t, len(t.rows), N=1).divide(minnie=rstop(t))
print "Where done"
# Organizing
NDLeafs = m.nonPrunedLeaves() # The surviving non-dominated leafs
allLeafs = m.nonPrunedLeaves() + m.prunedLeaves() # All of the leafs
# After mutation: Check how many rows were actually evaluated
numEval = 0
for leaf in allLeafs:
for row in leaf.table.rows:
if row.evaluated:
numEval += 1
# After mutation; Convert back to JMOO Data Structures
population = []
for leaf in NDLeafs:
for row in leaf.table.rows:
if row.evaluated:
population.append(jmoo_individual(problem, [x for x in row.cells[:len(problem.decisions)]],
[x for x in row.cells[len(problem.decisions):]]))
else:
indi = jmoo_individual(problem, [x for x in row.cells[:len(problem.decisions)]], None)
indi.fitness.fitness = problem.evaluate(indi.decisionValues)
population.append(indi)
numEval += 1
# median_values = []
# for i in xrange(len(problem.objectives)):
# temp_values = []
# for pop in population:
# temp_values.append(pop.fitness.fitness[i])
# median_values.append(median(temp_values))
#
# print median_values
return population, numEval