mattia wrote: > Il Fri, 06 Mar 2009 14:06:14 +0100, Peter Otten ha scritto: > >> mattia wrote: >> >>> Hi, I'm new to python, and as the title says, can I improve this >>> snippet (readability, speed, tricks): >>> >>> def get_fitness_and_population(fitness, population): >>> return [(fitness(x), x) for x in population] >>> >>> def selection(fitness, population): >>> ''' >>> Select the parent chromosomes from a population according to their >>> fitness (the better fitness, the bigger chance to be selected) ''' >>> selected_population = [] >>> fap = get_fitness_and_population(fitness, population) pop_len = >>> len(population) >>> # elitism (it prevents a loss of the best found solution) # take >>> the only 2 best solutions >>> elite_population = sorted(fap) >>> selected_population += [elite_population[pop_len-1][1]] + >>> [elite_population[pop_len-2][1]] >>> # go on with the rest of the elements for i in range(pop_len-2): >>> # do something >> >> def selection1(fitness, population, N=2): >> rest = sorted(population, key=fitness, reverse=True) best = rest[:N] >> del rest[:N] >> # work with best and rest >> >> >> def selection2(fitness, population, N=2): >> decorated = [(-fitness(p), p) for p in population] >> heapq.heapify(decorated) >> >> best = [heapq.heappop(decorated)[1] for _ in range(N)] rest = [p for >> f, p in decorated] >> # work with best and rest >> >> Both implementations assume that you are no longer interested in the >> individuals' fitness once you have partitioned the population in two >> groups. >> >> In theory the second is more efficient for "small" N and "large" >> populations. >> >> Peter > > Ok, but the fact is that I save the best individuals of the current > population, than I'll have to choose the others elements of the new > population (than will be N-2) in a random way. The common way is using a > roulette wheel selection (based on the fitness of the individuals, if the > total fitness is 200, and one individual has a fitness of 10, that this > individual will have a 0.05 probability to be selected to form the new > population). So in the selection of the best solution I have to use the > fitness in order to get the best individual, the last individual use the > fitness to have a chance to be selected. Obviously the old population anf > the new population must have the same number of individuals.
You're right, it was a bad idea. Peter -- http://mail.python.org/mailman/listinfo/python-list
