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
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
genome.mutator.set(Mutators.G1DBinaryStringMutatorFlip)
#genome.initializator.set(chromosomes_init)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.selector.set(Selectors.GRankSelector)
ga.setCrossoverRate(0.75)
ga.setMutationRate(0.02)
ga.setElitism(True)
ga.setGenerations(100)
ga.terminationCriteria.set(GSimpleGA.ConvergenceCriteria)
sqlite_adapter = DBAdapters.DBSQLite(identify="ex10", resetDB=True)
ga.setDBAdapter(sqlite_adapter)
# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=10)
# Best individual
best = ga.bestIndividual()
#print best
i, nearest_supers = eval_func(best, graph=True)
title = r'$Rank \ Selection$'
GAgraph.generate_graph(list(best.getBinary()), nearest_supers, FIELD_WIDTH,
FIELD_HEIGHT, coords, 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)
