def testEvalIndividualCapacitySufficient(self):
''' test on individual that offers more than enough capacity to handle all requests
'''
pop = toolBox.toolbox.population(n=2)
fit = Fitness()
ind1 = pop[0]
ind2 = pop[1]
for i, item in enumerate(ind1):
ind1[i][1] = 120
self.assertGreater(fit.evalIndividualCapacity(ind2), fit.evalIndividualCapacity(ind1))
def testEvalIndividualCapacityZeroCapacity(self):
''' test worst case scenario - individual with zero capacity has the worst fitness
'''
pop = toolBox.toolbox.population(n=2)
fit = Fitness()
ind1 = pop[0]
ind2 = pop[1]
for i, item in enumerate(ind1):
ind1[i][1] = 0
self.assertGreater(fit.evalIndividualCapacity(ind1), fit.evalIndividualCapacity(ind2))
def testEvalIndividualCapacityNotZero(self):
''' test that no one's perfect
'''
pop = toolBox.toolbox.population(n=2)
fit = Fitness()
self.assertGreater(fit.evalIndividualCapacity(pop[0]), 0)
def testEvalIndividualCapacity(self):
''' test to check that no individual is assigned a fitness value less than 0
'''
pop = toolBox.toolbox.population(n=2)
fit = Fitness()
self.assertFalse(fit.evalIndividualCapacity(pop[0]) < 0)
def main():
fitnessClass =Fitness()
# Generate the population
pop = toolBox.toolbox.population(n=POPULATION_SIZE)
fitnessClass.evalIndividualCapacity(pop[0])
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
pop, log = algorithms.eaSimple(pop, toolBox.toolbox, cxpb=CROSS_OVER_PROB,
mutpb=MUTATION_PROB, ngen=NO_OF_GENERATION, stats=stats,
halloffame=hof, verbose=True)
## Evaluate the entire population
#fitnesses = list(map(toolBox.toolbox.evaluate, pop))
#for ind, fit in zip(pop, fitnesses):
# ind.fitness.values = fit
# Iterate trough a number of generations
# for g in range(NGEN):
# print("-- Generation %i --" % g)
# # Select individuals based on their fitness
# offspring = toolBox.toolbox.select(pop, len(pop))
# # Cloning those individuals into a new population
# offspring = list(map(toolBox.toolbox.clone, offspring))
# # Calling the crossover function
# crossover(offspring)
# mutation(offspring)
# invalidfitness(offspring)
# The Best Individual found
best_ind = tools.selBest(pop, 1)[0]
print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
generateTimeTable(best_ind)
