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. -- http://mail.python.org/mailman/listinfo/python-list
