class TestPopulation(unittest.TestCase):
def setUp(self):
turns = ['F', 'L', 'L', 'F', 'R', 'F', 'R']
values = ['P', 'P', 'H', 'P', 'H', 'H', 'P']
self.turnList1 = TurnList(turns, values)
turns2 = ['R', 'L', 'L', 'F', 'L', 'F', 'R']
values2 = ['H', 'P', 'H', 'P', 'H', 'H', 'P']
self.turnList2 = TurnList(turns2, values2)
turns3 = ['F', 'L', 'L', 'F', 'R', 'F', 'R']
values3 = ['P', 'P', 'H', 'P', 'H', 'H', 'P']
self.turnList3 = TurnList(turns3, values3)
bigValues1 = list("PPHPPHHPPHHPPPPPHHHHHHHHHHPPPPPPHHPPHHPPHPPHHHHH")
bigTurns1 = list("FRFRLFLFRFLFFRLFLRRLLRRFRFLFRLLRFRLFLFRLFFFFRLLF")
self.bigTL1 = TurnList(bigTurns1, bigValues1)
bigValues2 = list("PPHPPHHPPHHPPPPPHHHHHHHHHHPPPPPPHHPPHHPPHPPHHHHH")
bigTurns2 = list("FRFRLFLFRFLFFRLFLRRLLRRFRFLFRLLRFRLFLFRLFFFFRLLF")
self.bigTL2 = TurnList(bigTurns2, bigValues2)
self.pop1 = Population([self.turnList1],
[self.turnList1.calculateFitness()])
self.pop2 = Population([self.turnList2],
[self.turnList2.calculateFitness()])
self.pop3 = Population([self.turnList3],
[self.turnList3.calculateFitness()])
self.bigPop1 = Population([self.bigTL1],
[self.bigTL1.calculateFitness()])
self.bigPop2 = Population([self.bigTL2],
[self.bigTL2.calculateFitness()])
self.emptyPopulation = Population()
def testAppendGene(self):
self.pop1.appendGene(self.turnList2)
self.assertEquals(self.pop1.getGene(1), self.turnList2)
self.pop2.appendGene(self.turnList1)
self.assertEquals(self.pop2.getGene(1), self.turnList1)
self.pop2.appendGene(self.turnList1)
def testAppendPopulation(self):
self.pop1.appendPopulation(self.pop2)
self.assertEquals(self.pop1.getGene(1), self.turnList2)
self.assertTrue(self.pop1.size() == 2)
self.pop2.appendPopulation(self.pop1)
self.assertEquals(self.pop2.getGene(1), self.turnList1)
self.assertEquals(self.pop2.getGene(2), self.turnList2)
self.assertTrue(self.pop2.size() == 3)
# Since these are pass-by-reference, updates to one member of the population
# can result in updates to different members of the population.
self.pop2.appendPopulation(self.pop2)
self.pop2.getGene(2).setTurn(0, 'L')
def testGetGene(self):
self.assertEquals(self.pop1.getGene(0), self.turnList1)
self.assertEquals(self.pop2.getGene(0), self.turnList2)
def testGetFitness(self):
self.assertEquals(self.pop1.getFitness(0),
self.turnList1.calculateFitness())
self.assertEquals(self.pop2.getFitness(0),
self.turnList2.calculateFitness())
def testSize(self):
self.assertEquals(self.pop1.size(), 1)
self.assertEquals(self.pop2.size(), 1)
def testMutatatePopulationWithIncreaseAndCooling(self):
print "Ensure advanced mutation algorithm is increase fitness faster than simple mutations."
print "{:10s} {:10s}".format("Normal", "Advanced")
for gen in range(100):
self.bigPop1.mutatePopulation(1)
self.bigPop2.mutatePopulationWithIncreaseAndCooling(1, gen)
print "{:10d} {:10d}".format(self.bigPop1.getFitness(0),
self.bigPop2.getFitness(0))
self.assertTrue(
self.bigPop1.getFitness(0) <= self.bigPop2.getFitness(0))
class GeneticAlgorithm:
"""
An iterator that implements the genetic algorithm
on a population of TurnList genes.
"""
def __init__(self,
sequenceOfValues,
populationSize=100,
generations=200,
elitePercent=.10,
crossoverPercent=.80,
mutationPercent=.25):
self.sequenceOfValues = sequenceOfValues # Values
self.geneLength = len(sequenceOfValues) # Length of a gene
self.populationSize = populationSize # Size of the Population
self.generations = generations # Number of generations to run.
self.elitePercent = elitePercent # Percent of genes to preserve as elite.
self.crossoverPercent = crossoverPercent # Percent of genes to crossover.
self.mutationPercent = mutationPercent # Percent of gene turns to mutate.
self.currentPopulation = Population(
genes=[], fitness=[]) # Stores the current Population
self.newPopulation = Population(
genes=[], fitness=[]
) # Temporary Population used to construct next new population.
self.elites = Population(
genes=[], fitness=[]
) # The elites set aside while constructing the new population.
self.nonElites = Population(
genes=[], fitness=[]
) # The non-elites identified from the previous generation.
# Crossed and Uncrossed have references to genes
# from the previous generations - but they will be in their
# mutated form. Use these Population sets for access to
# crossedover indexes and locations.
self.crossed = Population(
genes=[], fitness=[]
) # The genes which have been crossed over for the new generation.
self.uncrossed = Population(
genes=[], fitness=[]
) # The genes which where not crossed for the new generation.
# Mutations is the final form of the current generation, minus the
# elites. Use for the information about mutation locations.
self.mutations = Population(
genes=[], fitness=[]
) # The crossed and uncrossed genes combined for mutation.
self.history = []
def __iter__(self):
return self
def next(self):
"""
Performs the genetic algorithm on the previous population to construct
a new population.
@return: int the current generation.
"""
generation = len(self.history)
# If this is the first iteration of the genetic algorithm,
# a new, random population needs to be created.
if len(self.history) == 0:
self.initializeOriginalPopulation()
self.history.append(self.currentPopulation.getGene(0))
return generation
# For >1 generations, perform the following steps:
elif len(self.history) < self.generations:
# Prepares an empty new population.
self.newPopulation = Population(genes=[], fitness=[])
# 1) Identify the elites.
self.pullElites()
# 2) Perform crossover on the non-elites.
self.createCrossover()
# 3) Perform mutations on the non-elites.
self.createMutations()
# 4) Construct a new population from the elites and non-elites.
self.createNewPopulation()
# 5) Assign the new population to the current population.
self.currentPopulation = self.newPopulation
# Create a history of the highest scoring member of the
# current population.
self.history.append(self.currentPopulation.getGene(0))
return generation
else:
raise StopIteration
def initializeOriginalPopulation(self):
"""
Creates an original population from the values provided
to this GeneticAlgorithm.
"""
for __ in range(self.populationSize):
valid = False
while not valid:
# 1) Produce a sequence of turns using the MonteCarlo algorithm.
sequenceOfTurns = montecarlo.getDefaultMCSequence(
self.geneLength)
# 2) Create a TurnList from the sequence of turns and
# their corresponding values.
turnList = TurnList(sequenceOfTurns, self.sequenceOfValues)
# 3) Determine if the sequence of turns is valid.
if not turnList.toCoordinates().hasConflict():
self.currentPopulation.appendGene(turnList)
valid = True
def pullElites(self):
"""
Construct a set of elite members of the population and
a set of non-elite members of the population.
"""
numElites = int(self.populationSize * self.elitePercent)
self.elites, self.nonElites = self.currentPopulation.splitElite(
numElites)
def createCrossover(self):
"""
Apply Crossover to the non-elite members of the current
population.
NOTE: New instances of the genes are created.
"""
numCrossovers = int(self.crossoverPercent * self.populationSize / 2)
#self.crossed, self.uncrossed = self.nonElites.createCrossoverPopulation(numCrossovers)
self.crossed, self.uncrossed = self.nonElites.createCrossoverPopulationWithIncreaseAndCooling(
numCrossovers, len(self.history))
def createMutations(self):
"""
Mutate members of the current population which have
been set aside as non-elite.
NOTE: crossed-over genes have their new instances mutated,
however the non-crossed-over non-elites will have
their original genes (those in the current population)
effected.
"""
self.mutations = Population(genes=[], fitness=[])
self.mutations.appendPopulation(self.crossed)
self.mutations.appendPopulation(self.uncrossed)
numMutations = (self.mutationPercent * self.mutations.size() *
len(self.mutations.getGene(0)))
#self.mutations.mutatePopulation(numMutations)
self.mutations.mutatePopulationWithIncreaseAndCooling(
numMutations, len(self.history))
def createNewPopulation(self):
"""
Combines the elite genes of the current generation
with those non-elite genes which have been crossed over
and mutated. Stores them in a new population.
"""
self.newPopulation = Population(genes=[], fitness=[])
self.newPopulation.appendPopulation(self.elites)
self.newPopulation.appendPopulation(self.mutations)
def displayStateChanges(self):
"""
A visually useful manner of inspecting the current generation of
this Genetic Algorithm.
* The first row references the indexes of the parents
from which the current gene has been crossed-over.
* The second row provides indexes for the genes of the current
generation. An "!" indicates the gene was produced from a crossover
operation that did not complete successfully. A "x" indicates
that the gene was the product of a crossover operation.
* The following rows indicate the turn state of the gene at a particular
cell. A turn surrounded by "<" and ">" indicates a cross-over location
from the previous generation. An "!" indicates a mutation created this cell.
* The final row indicates the fitness of the gene. A "*" indicates an elite
gene that was carried over directly from the previous generation.
"""
I = (len(str(self.populationSize - 1)) * 2) + 1
if I < 5:
I = 5
crossedGenes = self.crossed.crossedOverIndexes.keys()
numElites = int(self.populationSize * self.elitePercent)
results = "\n"
# Identify each gene's cross-over parents (if they had any).
for geneIndex in range(len(self.currentPopulation.genes)):
if self.currentPopulation.genes[geneIndex] in crossedGenes:
sourceIndexes = self.crossed.crossedOverIndexes[
self.currentPopulation.genes[geneIndex]]
results += str(
str(sourceIndexes[0] + numElites) + "x" +
str(sourceIndexes[1] + numElites)).center(I) + "|"
else:
results += str(" " * I) + "|"
results += '\n'
# Identify the gene's current index, whether it was the product of a crossover
# operation, and whether that crossover operation was "bad".
for geneIndex in range(len(self.currentPopulation.genes)):
badCross = False
cross = False
if self.currentPopulation.genes[
geneIndex] in self.nonElites.cheapCrosses:
badCross = True
if self.currentPopulation.genes[geneIndex] in crossedGenes:
cross = True
placed = ""
if badCross:
placed += "!"
placed += str(geneIndex)
if cross:
placed += "x"
results += placed.center(I) + "|"
results += '\n'
# A divider.
for geneIndex in range(len(self.currentPopulation.genes)):
results += str("-" * I) + "|"
results += "\n"
# Displays rows for the cell values of each gene, indicating
# crossover points and mutations.
for cellIndex in range(len(self.currentPopulation.genes[0])):
for geneIndex in range(len(self.currentPopulation.genes)):
currentGene = self.currentPopulation.genes[geneIndex]
mutation = False
crossed = False
if (self.mutations.mutationLocations.has_key(currentGene)
and cellIndex
in self.mutations.mutationLocations[currentGene]):
mutation = True
if (currentGene in crossedGenes
and self.crossed.crossedOverLocations[currentGene]
== cellIndex):
crossed = True
placed = ""
if crossed:
placed += "<"
placed += str(
self.currentPopulation.genes[geneIndex].get(cellIndex)[0])
if mutation:
placed += "!"
if crossed:
placed += ">"
results += placed.center(I) + "|"
results += '\n'
# Another divider.
for geneIndex in range(len(self.currentPopulation.genes)):
results += str("-" * I) + "|"
results += "\n"
# Display the fitness value for each gene, indicating whether the gene was elite
# and carried directly from the previous generation.
for geneIndex in range(len(self.currentPopulation.genes)):
if self.currentPopulation.genes[geneIndex] in self.elites.genes:
results += str(
str(self.currentPopulation.fitness[geneIndex]) +
"*").center(I) + "|"
else:
results += str(
self.currentPopulation.fitness[geneIndex]).center(I) + "|"
return results + '\n'
class GeneticAlgorithm:
"""
An iterator that implements the genetic algorithm
on a population of TurnList genes.
"""
def __init__(self, sequenceOfValues,
populationSize=100,
generations=200,
elitePercent=.10,
crossoverPercent=.80,
mutationPercent=.25):
self.sequenceOfValues = sequenceOfValues # Values
self.geneLength = len(sequenceOfValues) # Length of a gene
self.populationSize = populationSize # Size of the Population
self.generations = generations # Number of generations to run.
self.elitePercent = elitePercent # Percent of genes to preserve as elite.
self.crossoverPercent = crossoverPercent # Percent of genes to crossover.
self.mutationPercent = mutationPercent # Percent of gene turns to mutate.
self.currentPopulation = Population(genes=[], fitness=[]) # Stores the current Population
self.newPopulation = Population(genes=[], fitness=[]) # Temporary Population used to construct next new population.
self.elites = Population(genes=[], fitness=[]) # The elites set aside while constructing the new population.
self.nonElites = Population(genes=[], fitness=[]) # The non-elites identified from the previous generation.
# Crossed and Uncrossed have references to genes
# from the previous generations - but they will be in their
# mutated form. Use these Population sets for access to
# crossedover indexes and locations.
self.crossed = Population(genes=[], fitness=[]) # The genes which have been crossed over for the new generation.
self.uncrossed = Population(genes=[], fitness=[]) # The genes which where not crossed for the new generation.
# Mutations is the final form of the current generation, minus the
# elites. Use for the information about mutation locations.
self.mutations = Population(genes=[], fitness=[]) # The crossed and uncrossed genes combined for mutation.
self.history = []
def __iter__(self):
return self
def next(self):
"""
Performs the genetic algorithm on the previous population to construct
a new population.
@return: int the current generation.
"""
generation = len(self.history)
# If this is the first iteration of the genetic algorithm,
# a new, random population needs to be created.
if len(self.history) == 0:
self.initializeOriginalPopulation()
self.history.append(self.currentPopulation.getGene(0))
return generation
# For >1 generations, perform the following steps:
elif len(self.history) < self.generations:
# Prepares an empty new population.
self.newPopulation = Population(genes=[], fitness=[])
# 1) Identify the elites.
self.pullElites()
# 2) Perform crossover on the non-elites.
self.createCrossover()
# 3) Perform mutations on the non-elites.
self.createMutations()
# 4) Construct a new population from the elites and non-elites.
self.createNewPopulation()
# 5) Assign the new population to the current population.
self.currentPopulation = self.newPopulation
# Create a history of the highest scoring member of the
# current population.
self.history.append(self.currentPopulation.getGene(0))
return generation
else:
raise StopIteration
def initializeOriginalPopulation(self):
"""
Creates an original population from the values provided
to this GeneticAlgorithm.
"""
for __ in range(self.populationSize):
valid = False
while not valid:
# 1) Produce a sequence of turns using the MonteCarlo algorithm.
sequenceOfTurns = montecarlo.getDefaultMCSequence(self.geneLength)
# 2) Create a TurnList from the sequence of turns and
# their corresponding values.
turnList = TurnList(sequenceOfTurns, self.sequenceOfValues)
# 3) Determine if the sequence of turns is valid.
if not turnList.toCoordinates().hasConflict():
self.currentPopulation.appendGene(turnList)
valid = True
def pullElites(self):
"""
Construct a set of elite members of the population and
a set of non-elite members of the population.
"""
numElites = int(self.populationSize * self.elitePercent)
self.elites, self.nonElites = self.currentPopulation.splitElite(numElites)
def createCrossover(self):
"""
Apply Crossover to the non-elite members of the current
population.
NOTE: New instances of the genes are created.
"""
numCrossovers = int(self.crossoverPercent * self.populationSize / 2 )
#self.crossed, self.uncrossed = self.nonElites.createCrossoverPopulation(numCrossovers)
self.crossed, self.uncrossed = self.nonElites.createCrossoverPopulationWithIncreaseAndCooling(numCrossovers, len(self.history))
def createMutations(self):
"""
Mutate members of the current population which have
been set aside as non-elite.
NOTE: crossed-over genes have their new instances mutated,
however the non-crossed-over non-elites will have
their original genes (those in the current population)
effected.
"""
self.mutations = Population(genes=[], fitness=[])
self.mutations.appendPopulation(self.crossed)
self.mutations.appendPopulation(self.uncrossed)
numMutations = (self.mutationPercent * self.mutations.size() * len(self.mutations.getGene(0)))
#self.mutations.mutatePopulation(numMutations)
self.mutations.mutatePopulationWithIncreaseAndCooling(numMutations, len(self.history))
def createNewPopulation(self):
"""
Combines the elite genes of the current generation
with those non-elite genes which have been crossed over
and mutated. Stores them in a new population.
"""
self.newPopulation = Population(genes=[], fitness=[])
self.newPopulation.appendPopulation(self.elites)
self.newPopulation.appendPopulation(self.mutations)
def displayStateChanges(self):
"""
A visually useful manner of inspecting the current generation of
this Genetic Algorithm.
* The first row references the indexes of the parents
from which the current gene has been crossed-over.
* The second row provides indexes for the genes of the current
generation. An "!" indicates the gene was produced from a crossover
operation that did not complete successfully. A "x" indicates
that the gene was the product of a crossover operation.
* The following rows indicate the turn state of the gene at a particular
cell. A turn surrounded by "<" and ">" indicates a cross-over location
from the previous generation. An "!" indicates a mutation created this cell.
* The final row indicates the fitness of the gene. A "*" indicates an elite
gene that was carried over directly from the previous generation.
"""
I = (len(str(self.populationSize-1)) * 2) + 1
if I < 5:
I = 5
crossedGenes = self.crossed.crossedOverIndexes.keys()
numElites = int(self.populationSize * self.elitePercent)
results = "\n"
# Identify each gene's cross-over parents (if they had any).
for geneIndex in range(len(self.currentPopulation.genes)):
if self.currentPopulation.genes[geneIndex] in crossedGenes:
sourceIndexes = self.crossed.crossedOverIndexes[self.currentPopulation.genes[geneIndex]]
results += str( str(sourceIndexes[0] + numElites) + "x" + str(sourceIndexes[1] + numElites) ).center(I) + "|"
else:
results += str(" " * I) + "|"
results += '\n'
# Identify the gene's current index, whether it was the product of a crossover
# operation, and whether that crossover operation was "bad".
for geneIndex in range(len(self.currentPopulation.genes)):
badCross = False
cross = False
if self.currentPopulation.genes[geneIndex] in self.nonElites.cheapCrosses:
badCross = True
if self.currentPopulation.genes[geneIndex] in crossedGenes:
cross = True
placed = ""
if badCross:
placed += "!"
placed += str(geneIndex)
if cross:
placed += "x"
results += placed.center(I) + "|"
results += '\n'
# A divider.
for geneIndex in range(len(self.currentPopulation.genes)):
results += str("-" * I) + "|"
results += "\n"
# Displays rows for the cell values of each gene, indicating
# crossover points and mutations.
for cellIndex in range(len(self.currentPopulation.genes[0])):
for geneIndex in range(len(self.currentPopulation.genes)):
currentGene = self.currentPopulation.genes[geneIndex]
mutation = False
crossed = False
if (self.mutations.mutationLocations.has_key(currentGene) and
cellIndex in self.mutations.mutationLocations[currentGene]):
mutation = True
if (currentGene in crossedGenes and
self.crossed.crossedOverLocations[currentGene] == cellIndex):
crossed = True
placed = ""
if crossed:
placed += "<"
placed += str(self.currentPopulation.genes[geneIndex].get(cellIndex)[0])
if mutation:
placed += "!"
if crossed:
placed += ">"
results += placed.center(I) + "|"
results += '\n'
# Another divider.
for geneIndex in range(len(self.currentPopulation.genes)):
results += str("-" * I) + "|"
results += "\n"
# Display the fitness value for each gene, indicating whether the gene was elite
# and carried directly from the previous generation.
for geneIndex in range(len(self.currentPopulation.genes)):
if self.currentPopulation.genes[geneIndex] in self.elites.genes:
results += str(str(self.currentPopulation.fitness[geneIndex]) + "*").center(I) + "|"
else:
results += str(self.currentPopulation.fitness[geneIndex]).center(I) + "|"
return results + '\n'