def evolve_callback(ga_engine):
global n
best = ga_engine.bestIndividual()
i, nearest_supers = eval_func(best, graph=True)
title = 'Best Chromosome (Gen. %03d)' % n
GAgraph.generate_graph(list(best.getBinary()), nearest_supers, FIELD_WIDTH, FIELD_HEIGHT, title,n)
n += 1
def evolve_callback(ga_engine):
global n
best = ga_engine.bestIndividual()
i, nearest_supers = eval_func(best, graph=True)
title = 'Best Chromosome (Gen. %03d)' % n
GAgraph.generate_graph(list(best.getBinary()), nearest_supers, FIELD_WIDTH,
FIELD_HEIGHT, title, n)
n += 1
def run_main():
# Define chromosome
genome = G1DBinaryString.G1DBinaryString(NUM_NODES)
genome.evaluator.set(eval_func)
genome.mutator.set(Mutators.G1DBinaryStringMutatorFlip)
genome.crossover.set(Crossovers.G1DBinaryStringXSinglePoint)
# Initialize Genetic algorithm
ga = GSimpleGA.GSimpleGA(genome)
# Utilize all processors if more than one are available
ga.setMultiProcessing(True)
# Set population size
ga.setPopulationSize(POP_SIZE)
# Set generation at which evolution pauses and interactive mode starts
#ga.setInteractiveGeneration(500)
# Selection
ga.selector.set(Selectors.GTournamentSelector)
#ga.selector.set(Selectors.GRankSelector)
#ga.selector.set(Selectors.GUniformSelector)
#ga.selector.set(Selectors.GRouletteWheel)
# Set Probabilities for Genetic Algorithm
ga.setCrossoverRate(Pc)
ga.setMutationRate(Pm)
ga.setElitism(True)
ga.setElitismReplacement(NUM_NODES / 4)
ga.setGenerations(NUM_GENERATIONS)
# Set up database for data storage and graphing with pyevolve_graph.py
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex10", resetDB=True)
#adapter = DBAdapters.DBVPythonGraph(identify="run_01", frequency = 1)
#ga.setDBAdapter(adapter)
# Run genetic Algorithm
ga.evolve(freq_stats=NUM_GENERATIONS / 4)
# Show best chromosome
best = ga.bestIndividual()
print
print "Best Chromosome:", '\n', best.getBinary()
print "Fitness Raw Score:", best.getRawScore()
print
# Graph best chromosome
i, nearest_supers = eval_func(best, graph=True)
title = r'$%d \ Nodes$' % NUM_NODES
def run_main():
# Define chromosome
genome = G1DBinaryString.G1DBinaryString(NUM_NODES)
genome.evaluator.set(eval_func)
genome.mutator.set(Mutators.G1DBinaryStringMutatorFlip)
genome.crossover.set(Crossovers.G1DBinaryStringXSinglePoint)
# Initialize Genetic algorithm
ga = GSimpleGA.GSimpleGA(genome)
# Utilize all processors if more than one are available
ga.setMultiProcessing(True)
# Set population size
ga.setPopulationSize(POP_SIZE)
# Set generation at which evolution pauses and interactive mode starts
#ga.setInteractiveGeneration(500)
# Selection
ga.selector.set(Selectors.GTournamentSelector)
#ga.selector.set(Selectors.GRankSelector)
#ga.selector.set(Selectors.GUniformSelector)
#ga.selector.set(Selectors.GRouletteWheel)
# Set Probabilities for Genetic Algorithm
ga.setCrossoverRate(Pc)
ga.setMutationRate(Pm)
ga.setElitism(True)
ga.setElitismReplacement(NUM_NODES/4)
ga.setGenerations(NUM_GENERATIONS)
# Set up database for data storage and graphing with pyevolve_graph.py
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex10", resetDB=True)
#adapter = DBAdapters.DBVPythonGraph(identify="run_01", frequency = 1)
#ga.setDBAdapter(adapter)
# Run genetic Algorithm
ga.evolve(freq_stats=NUM_GENERATIONS/4)
# Show best chromosome
best = ga.bestIndividual()
print
print "Best Chromosome:", '\n', best.getBinary()
print "Fitness Raw Score:", best.getRawScore()
print
# Graph best chromosome
i, nearest_supers = eval_func(best, graph=True)
title = r'$%d \ Nodes$' % NUM_NODES
# Set Probabilities for Genetic Algorithm
ga.setCrossoverRate(Pc)
ga.setMutationRate(Pm)
ga.setElitism(True)
ga.setElitismReplacement(NUM_NODES/4)
ga.setGenerations(NUM_GENERATIONS)
# Set up database for data storage and graphing with pyevolve_graph.py
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex10", resetDB=True)
#adapter = DBAdapters.DBVPythonGraph(identify="run_01", frequency = 1)
#ga.setDBAdapter(adapter)
# Run genetic Algorithm
ga.evolve(freq_stats=NUM_GENERATIONS/4)
# Show best chromosome
best = ga.bestIndividual()
print
print "Best Chromosome:", '\n', best.getBinary()
print "Fitness Raw Score:", best.getRawScore()
print
# Graph best chromosome
i, nearest_supers = eval_func(best, graph=True)
title = 'Node Placement'# % NUM_NODES
GAgraph.generate_graph(list(best.getBinary()), nearest_supers, FIELD_WIDTH, FIELD_HEIGHT, title)
# Set Probabilities for Genetic Algorithm
ga.setCrossoverRate(Pc)
ga.setMutationRate(Pm)
ga.setElitism(True)
ga.setElitismReplacement(NUM_NODES / 4)
ga.setGenerations(NUM_GENERATIONS)
# Set up database for data storage and graphing with pyevolve_graph.py
#sqlite_adapter = DBAdapters.DBSQLite(identify="ex10", resetDB=True)
#adapter = DBAdapters.DBVPythonGraph(identify="run_01", frequency = 1)
#ga.setDBAdapter(adapter)
# Run genetic Algorithm
ga.evolve(freq_stats=NUM_GENERATIONS / 4)
# Show best chromosome
best = ga.bestIndividual()
print
print "Best Chromosome:", '\n', best.getBinary()
print "Fitness Raw Score:", best.getRawScore()
print
# Graph best chromosome
i, nearest_supers = eval_func(best, graph=True)
title = 'Node Placement' # % NUM_NODES
GAgraph.generate_graph(list(best.getBinary()), nearest_supers, FIELD_WIDTH,
FIELD_HEIGHT, title)